MetaSEEk: A Content-Based Meta-Search Engine for Images
`
`Mandis Beigi, Ana B. Benitez, andShih-Fu Chang
`Department of Electrical Engineering & New Media Technology Center
`Columbia University, New York, NY 10027
`
`ABSTRACT
`
`Search engines are the most powerful resources for finding information on the rapidly expanding World Wide Web (WWW).
`Finding the desired search engines and learning how to use them, however, can be very time consuming. The integration of
`such search tools enables the users to access information across the world in a transparent and efficient manner. These
`systems are called meta-search engines. The recent emergence of visual information retrieval (VIR) search engines on the
`web is leading to the same efficiency problem. This paper describes and evaluates MetaSEEk, a content-based meta-search
`engine used for finding images on the Web based on their visual information. MetaSEEk is designed to intelligently select
`and interface with multiple on-line image search engines by ranking their performance for different classes of user queries.
`User feedback is also integrated in the ranking refinement. We compare MetaSEEk with a base line version of meta-search
`engine, which does not use the past performance of the different search engines in recommending target search engines for
`future queries.
`
`Keywords: MetaSEEk, meta-search engine, content-based visual query, color search, texture search, performance
`monitoring, World Wide Web
`
`1. INTRODUCTION
`
`The explosive growth of the World Wide Web has motivated the development of many search engines to assist the
`unmanageable task of navigating the Web. They try to satisfy the users' information needs for newspaper articles, software,
`movie reviews, books, music recording, images, video, etc. Two types of search engines can be found on the Web: large-
`scale robot-based and specialty search engines. Large-s cale search engines try to index the contents of the entire World
`Wide Web, but usually fail to disseminate between desired data and unneeded information. On the other hand, specialty
`search engines are more focussed databases, which can not be applied to general topics.
`
`Experienced users of the Internet would begin to query the appropriate specialty search engines to obtain desirable
`results, and continue querying general search engines when the specialized engines fail to yield helpful information.
`Nevertheless, the proliferation of search engines has replaced the problem of finding information on the Internet with the
`problem of knowing where search engines are, what they are designed to retrieve, and how to use them. Consequently,
`searching the Web for specific information has become a very time consuming and inefficient task for even the most expert
`users.
`
`This situation has motivated the recent research and development in integrated search or meta-search engines [ 1J.
`Meta-search engines serve as common gateways, which automatically link users to multiple or competitive search engines.
`They accept requests from users, sometimes, along with user-specified query plans to select target search engines. The meta-
`search engines may also keep track of the past performance of each search engine and use it in selecting target search engines
`for future queries. Many approaches have been proposed for meta-searching. Section 2 presents an overview of these
`approaches, the majority of which have been designed for text databases.
`
`Digital images and video are becoming an integral part of human communications {2]. The ease of creating and
`capturing digital imagery has trigged the recent development of visual information retrieval (VIR) systems on the web
`
`Further author information —
`M.B.: Email: mandis@ctr.columbia.edu
`A.B.C.: Email: ana@ctr.columbia.edu
`S.C.: Email: sfchang@ctr.columbia.edu
`
`118
`
`SPIE Vol. 3312. 0277-786X/971$1O.OO
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-1
`
`

`

`[3,4,5]. These systems usually provide methods for retrieving digital images by using examples and/or visual sketches. In
`order to query the visual repositories, the visual features of the imagery, such as colors, textures, shapes, etc, are used in
`combination with text and other related information. Everyday, users are finding new VIR systems on-line what leads, once
`more, to the problem of efficiently and effectively retrieving the information of interest.
`
`We have developed a prototype meta image search engine, MetaSEEk, to investigate the issues involved with
`efficiently querying large, distributed on-line visual information sources. Our meta-search engine, MetaSEEk, adopts the
`principle that Web resources should be used efficiently. For each query, MetaSEEk selects the target engines that may the
`desire results by weighing search tools' successes and failures in similar query conditions. The implementation of the meta-
`search engine is described in section 2.
`
`Section 3 describes the experiments, the evaluation measures and the comparison results between the MetaSEEk
`prototype and a base line search-engine that randomly selects the search engines to send the queries to. An interesting issue
`examined in MetaSEEk is the reliability of the selection and ranking of the remote search engines for different type of
`queries. Another important technical aspect is the heterogeneity among the different remote search engines and possible
`technical approaches to enhance interoperability. Finally, section 4 closes with concluding remarks and open issues for
`future research.
`
`2. RELATED RESEARCH
`
`Meta-search engines serve as common gateways, linking users to multiple search engines in a transparent manner. Working
`meta-search engines include three basic components, as depicted in Figure 1 [ 1]. The dispatching component selects target
`search engines for each query. The query interface component translates the user-specified query to compatible scripts to
`each target search engine. The display interface component merges the query results from each search engines, removes
`duplicates and displays them to the user in a uniform format.
`
`At the present time the wealth of meta-search engines on the WWW is still growing. Many approaches have been
`proposed for meta-searching. We overview a few of these efforts.
`
`The GlOSS (Glossary-of-Servers Server) project [6] uses a meta-index to estimate which databases are potentially
`most useful for a given query. This meta-index is constructed by integrating the indexes of each one of the target databases.
`For each database and each word, the number of documents containing that word is included in the meta-index. The two
`main drawbacks of this approach are: first, it requires each of the search engines to cooperate with the meta-searcher by
`supplying up-to-date indexing information, and second, as the number of databases increases, the administrative complexity
`may become prohibitive.
`
`The Harvest system [7] is being designed and built by the Internet Research Task Force Research Group on
`Resource Discovery. Harvest consists of several subsystems: a Gatherer collects indexing information and a Broker provides
`a flexible interface to this information. It is intended to be a scalable form of infrastructure for building and distributing
`content, indexing information, as well as for accessing Web information
`
`Wide Area Information Servers (WAIS) [8] divide its indices among the databases into multiple levels with the top-
`level index containing a "directory of servers". Given a query, the "directory of services" is searched and the query is then
`forwarded to selected databases.
`
`MetaCrawler [9] is a meta-search service developed at the University of Washington that integrates a set of general
`Web search engines. When a query is submitted, MetaCrawler dispatches queries to each one of those search engines,
`retrieves the HTML source of all the returned documents, and applies further analysis to clean up unavailable links and
`irrelevant documents. MetaCrawler obtains high precision but at the cost of network utilization.
`
`The SavvySearch meta-search tool [1] employs a meta-index approach for selecting relevant engines based on the
`terms in a user's query; previous experience about query successes and failures is tracked to enhance selection quality.
`SavvySearch selects resources for an individual user's query and balances resource consumption against expected result
`quality by querying the most relevant search engines first. Their experimental finding suggest that a meta-index approach
`
`119
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-2
`
`

`

`can be effective in making search engine selection decisions. However, the potentially large amount of knowledge required
`to make decisions raises some questions about the overall efficiency of the system.
`
`Meta Search Engine
`Query Dispatcher
`
`I
`
`Performance Monitor
`
`Display Interface
`Result Merger
`
`Format Converter
`
`Figure 1: Basic components of a meta-search engine
`
`Other automated Web meta-searchers are Dogpile, Metafind and Metasearch. These systems basically dispatch
`queries to each one of their search engines that they target and present the returned documents to the user in a uniform
`manner. Many manual query dispatch search engines are also available on the Web. Tools such as All-in-One, CUSI,
`search.com, Infi-Net's META search and InterNIC are essentially pages full of forms to sending queries to a number of
`different search engines. The selection process is entirely up to the user — they must type their query into a separate form for
`each query submission. Only one search engine is activated at a time, and the results appear in the native format of
`whichever search engine produced them.
`
`The ProFusion system [10] is a Web meta-search engine that supports both manual and automatic query dispatch. In
`automatic query dispatch, ProFusion analyzes the incoming queries, categorizes them, and automatically picks the best search
`engines for the query based on a priori knowledge (confidence factors) which represents the suitability of each search engine
`for each category. It uses these confidence factors to merge the search results into a re-weight list of the returned documents,
`removes duplicates and, optionally, broken links and presents the final rank-ordered list to the user. ProFusion's performance
`has been compared to the individual search engines and other meta searchers, demonstrating its ability to retrieve more
`relevant information and present fewer duplicate pages.
`
`120
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-3
`
`

`

`3. METASEEK
`
`MetaSEEk is an integrated search engine, which serves as a common gateway, linking users to multiple image search
`engines. It includes three main components as shown in Figure 1 . The query interface component accepts search queries
`from the user and translates them to the specific query interfaces used by each target search engine. The dispatching
`component decides which search engines the query should be sent to. The display component merges the results and ranks
`them for displaying. MetaSEEk evaluates the performance of each query method on a search engine for future queries based
`on the user's feedback.
`
`Queries can be submitted to MetaSEEk at http:llwww.ctr.columbia.edulMetaSEEk. The underlying system is
`implemented in C and currently runs on a HP platform. MetaSEEk uses socket programming for opening ports to send the
`queries to the individual target search engines and to download their results. HTTP commands are sent to the remote search
`engines in a similar manner to web browsers such as Netscape and Mosaic.
`
`3.1. Content based image query
`
`There are several methods, which may be used to retrieve images based on their visual contents. Several systems use visual
`features such as texture, color, shape, and structure [3,4,5]. For example, texture can describe the coarseness, contrast,
`roughness, and presence/absence of directionality of each image. Another method may be based on the amounts of different
`colors in each image. The color amounts can either be used to describe the entire color content of each image or they can
`describe the color amounts in local regions of the image. These methods can be used separately or can be combined in
`calculating the similarity measures for the content-based image query. Different search engines use various methods and
`support alternate combinations. They also use different algorithms for calculating the similarity measures and their distances.
`
`3.2. The query interface component
`
`MetaSEEk currently supports the following target search engines: VisualSEEk, WebSEEk, QBIC and Virage. In the current
`version of MetaSEEk, the user interface allows for browsing of random images retrieved from the remote search engines.
`The user can select a method for querying such as color and texture. These two methods can be selected individually or they
`can be combined. Another popular search technique used in the image search engines is search based on keywords. This
`kind of search is used in search engines for querying documents as well as images. Image search based on visual content
`usually returns a ranked list of images which have the highest similarity to the query input, which could be an example image
`or a visual sketch. Keyword-based search may be used to match images with particular subjects (e.g., nature and people) and
`narrow down the search scope.
`
`MetaSEEk allows a search based on example images, URLs or keyword text. No all the search engines support all
`these options. The query dispatching component of MetaSEEk makes the decision on which search engines the queries
`should be sent to. This component is explained in detail in the next subsection.
`
`The user can specify a value for the maximum waiting time which is used to prevent the query system from stalling
`if a target search engine happens to be down or unreachable. Figure 2 shows the user interface for the MetaSEEk search
`engine.
`
`3.3. The query dispatching component
`
`MetaSEEk queries the search engines that first, support the method of the query selected by the user (i.e. color and/or
`texture), and second, have high past performance scores. The performance scores are calculated every time a query is made
`and are based on the user's feedback. The calculation of the performance scores is explained in section 3.5.
`
`MetaSEEk keep track of the performance scores of the search engines with respect to each query on every image.
`These performance scores are indexed in the database and will be used to select the target search engines for each new query.
`MetaSEEk also stores the visual feature vector for each queried image in case the queried image is not already in the
`database. In this case, when the user issues a new query, a set of old queries with the most similar feature vectors with that of
`the new query will be used. The queried images' performance scores will be used to select the search engines for the new
`query. This approach basically finds the best query examples from the past and follows its route to selecting the remote
`search engines, which have done well for that past queries.
`
`121
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-4
`
`

`

`File Edit View Go Bookmarks OpUDnS Directory Window
`
`Go To: 1http I/ww. ctr. colwftbia. edu/)ietSEE
`
`Figure 2: The query interface of MetaSEEk
`
`122
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-5
`
`

`

`Once a query is made by selecting, or specifying the URL address of an image, MetaSEEk searches its database for
`the specified image to find the best search options based on the previous queries on that image. A search option is a query
`method on a specific search engine. For example, a query to the VisualSEEk search engine based on texture is considered as
`a search option. If the image is not found in the database, the best search options will be decided using the previous queries
`of the most similar images. The most similar images are selected from the database by comparing the locally calculated
`feature vectors for color and texture. The Tamura algorithm [1 1] is used for computing the texture feature vectors. For color,
`the feature vectors are calculated using the color histogram algorithm. The distance between two feature vectors is calculated
`using the Euclidean distance. These feature vectors will then be added to the database for future queries. Note that other
`feature vectors can be used if necessary. Our performance monitoring and search engine recommendation framework is
`general and can accommodate different feature vectors.
`
`The database used for monitoring the performance of the different search options contains the following
`information:
`
`The locally calculated feature vectors for color and texture.
`
`. The image name, which includes the complete URL address of an image located on a local or remote site.
`.
`. A list of the performance scores for all the possible search options. For example: 3:2, 1 :5, 0: 1 , - 1:3, -1 :4, -
`NA:O, NA:6, NA:7, NA:8. In descending order, the first integers correspond to the scores and the integers
`proceeded by the ':' signs correspond to the indices of the search options. For example, these indices may
`correspond to search options shown in Table 1.
`
`Index
`0
`1
`2
`3
`4
`5
`6
`7
`8
`
`Option
`QBIC texture
`QBIC color percentages
`QBIC color layout
`VisualSEEk color pçrcentages
`VisualSEEk color layout
`VisualSEEk texture
`Virage color
`Virge composition
`Virage texture
`
`Table 1: MetaSEEk indexing example
`
`As can be seen from the above example, the indices are sorted based on their scores. A score of NA means that the
`specific query option corresponding to that index is not available on the search engine and that it can not be used for making
`a query. Since some search engines do not have support for a search based on a URL address of an image outside their own
`database, they can only be queried when an image from their own database is selected. Therefore, for all the images outside
`of their databases, the scores corresponding to query options that relate to other search engines will be set to NA.
`
`3.4. The display component
`
`Once the results are retrieved from each individual search engine, they need to be merged and displayed to the user. These
`results are ranked in the order of the closest to the farthest match. MetaSEEk performs additional ranking of the returned
`images by using the query images' performance scores. The result images returned by each query option are interleaved
`before displaying them to the user. The performance scores will determine the order of the displayed images and the number
`of images in each interleaved group for each query's results. For example, if the images returned by two query options have
`performance scores of 2 and 1 ,MetaSEEk will continue to display 2 images from the query option having a score of 2, and 1
`image from the query option with a score of 1 until all the returned images are displayed to the user. MetaSEEk will also do
`some cleaning up by removing duplicates if necessary.
`
`The use of the above specific merging algorithm is not meant to replace the ranking algorithms used in each remote
`search engine. Instead, it is used to cope with the heterogeneity among different algorithms used in different search engines.
`
`123
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-6
`
`

`

`Unlike the consistent distance metrics used in text search engines, each visual search engine uses different algorithms and
`metrics. In order to evaluate the similarities of the returned images (from different engines) with the query input, a common
`set of features could be computed for all the returned images and be compared with the query input's. This option, however,
`would be too costly for the network since most of the images would have to be downloaded in order for the feature vectors to
`be computed. The selected merging method ignores the actual distances between the images, therefore it is possible for an
`image with a lower similarity measure to the input query to be displayed before an image with a higher similarity.
`
`3.5. Performance monitoring
`
`The dispatching component of the meta-search engine determines which query method on a remote search engine should be
`executed. As mentioned earlier, the decision is made based on the performance metrics calculated from the past queries and
`whether or not the target engine supports the specified method of the query. The performance metric is a signed integer
`where a positive number indicates a good performance and a negative number corresponds to a poor performance. The
`performance metrics of the query image is modified every time the user sends a query. A visit of an image increments by one
`the performance metric of the search engine that returns the visited image. If an image is not selected (i.e. no visit) the
`performance score remains unchanged. The user can also specify if he/she likes or dislikes a particular image, which will in
`turn increment, or decrement the performance metric of the corresponding search engines. These modifications of the
`database are shown on Table 2.
`
`Event
`Visit
`No visit
`Like
`Dislike
`
`Score
`+1
`0
`+1
`-1
`
`Table 2: The assigned values for the performance metrics
`
`As mentioned earlier, MetaSEEk removes all the duplicate images returned by different search options on different
`search engines. If the user, however, clicks on the like/dislike button of an image having a duplicate, MetaSEEk will
`increment/decrement the performance scores for all the search options that had returned that image even though the
`duplicates will not be all displayed to the user.
`
`4. EXPERIMENTS AND EVALUATION
`
`This first version of MetaSEEk has been developed with the primary objective of investigating whether or not our
`recommendations of search engines for incoming queries were appropriate. When a query is submitted to MetaSEEk, the
`system only queries those search options that have provided the most desirable results in the past, according to the
`information in the performance database.
`
`A set of experiments was conducted to evaluate the performance of MetaSEEk: the reliability of the selection and
`ranking of the remote search engines for different user queries. These experiments were intended to provide a quantitative
`measure of how useful and precise the performance. The goal is to find the best images that the user is searching for as
`quickly as possible in a small number of queries made by the user. Therefore, one experiment would involve taking note of
`the number of queries until a desired image or set of images is found as the size of the performance database grows in time.
`Another experiment would be to note what fraction of the returned images from the search engines the user likes/dislikes as
`the size of the performance database grows.
`
`We collected data and compared two different systems: the MetaSEEk and a base line meta-search engine. The
`MetaSEEk prototype includes all the advanced features mentioned in section 3. On the other hand, the base line meta-seach
`engine does not use any past performance of the different search engines in selecting the target search engines. For each
`incoming query, it randomly selects a set of search options and queries them. This system does not distinguish between new
`and past queries because it does not keep or use any past performance scores.
`
`124
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-7
`
`

`

`Since MetaSEEk keeps track of the performance results for every query, the number of queries made by the user for
`finding an image is expected to decrease as the size of the database grows and the system accumulates more knowledge.
`However, if the performance database is not used, the number of queries is expected to change randomly. The number of
`images the user likes is expected to grow and the number of disliked images is expected to decrease as the performance
`database grows in time. If the performance results are not used, these numbers will change randomly and are not expected to
`follow any particular patterns.
`
`The results of these experiments are shown on the graphs in Figure 3 and Figure 4 for the two different systems:
`MetaSEEk and the base line meta-search engine. All the necessary data for performing these experiments was taken in a
`few-days of duration. As can be seen from these graphs, recommendations based on past performance scores have made a
`considerable amount of improvement in MetaSEEk. The number of queries made by the user till the desired image is found
`tends to decrease as the knowledge of the system increases. Differently, random variations are observed in the case of the
`base line meta-search engine. The statistics for the like/dislike experiment do not show any special pattern or improvement
`for the MetaSEEk system. That can be caused by limited duration of our experiments. More thorough knowledge may be
`accumulated over a longer period of experiments. Currently, we are continuing conducting more of such experiments and
`establish a larger knowledge base.
`
`20
`
`18
`
`16
`
`12
`
`10
`
`MetaSJk
`—Base Line System
`
`0
`
`25 50 75 100 125 150 175
`Number of query sessions
`
`200 225 250
`
`Figure 3: Number of queries till wanted image is found for MetaSEEk and base line systems
`
`125
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-8
`
`

`

`1
`
`0.9
`
`0.8
`
`0.7
`
`0.6
`0.5
`E 0.4
`
`0.3
`
`0.2
`
`0.1
`
`0
`
`1
`
`0.9
`
`0.8
`
`0.7
`
`0.6
`
`0.5. 0.4
`
`0.3
`
`0.2
`
`0.1
`
`0
`
`126
`
`Likinj
`[—Disliking
`
`Likin—Disliking
`
`0
`
`50
`
`100 150 200 250 300
`Number of queries
`
`350 400 450 500
`
`Figure 4: a) Like/Dislike trend for MetaSEEk prototype
`
`0 20 40
`
`60 80 100 120 140 160 180 200 220 240
`Number of queries
`
`Figure 4: b) Like/Dislike trend for base line prototype
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-9
`
`

`

`5. CONCLUSION AND OPEN ISSUES
`
`The proliferation of text search engines on the Web has motivated the recent research in meta-search engines. In the same
`trend, impelled by the growing wealth of VIR systems on the WWW, we have developed a prototype meta image search
`engine, MetaSEEk, to explore the issues involved in querying large, distributed, on-line visual information system sources.
`Our goal is to investigate novel techniques for enhancing interoperability of distributed VIR systems, rather than ranking the
`performance of individual systems.
`
`MetaSEEk uses performance scores to recommend remote search engines and query methods to send the query to.
`These performance scores are constructed by accumulating their successes and failures in the past queries. When a query is
`submitted to MetaSEEk, the most relevant query methods and search engines are selected by weighing their performance
`records to predict the ones that are likely to produce relevant results. If MetaSEEk receives a new query not encountered
`before, the system will match it to the content of the database in order to obtain a list of the most similar past queries.
`Averaging their performance indexes, MetaSEEk is able to recommend suitable search engines. MetaSEEk updates the
`performance ranking with new user feedback.
`
`As the experiments show, the performance of a meta image search system can be greatly improved by implementing
`an intelligent integrated searching engine. This improvement involves the speed at which the desired images are found and
`how likely the user is satisfied with the results.
`
`For future versions of MetaSEEk, we are considering an alternative approach to relate the new queries to the past
`ones: to cluster the past queries into several classes each of which share similar visual features. Performance scores are
`averaged over each cluster of past queries. Each new query will be classified to the closest class, whose performance records
`will be used to recommend the remote search engines. Users may intervene with the selection process by providing some
`search plans, which will override the recommendation of the meta-search engine. The performance scores may be
`accumulated over all past queries or a limited recent period. Because of the transient status of the networks, accumulation
`over a short period might be more desirable. The period interval may depend on the characteristics of the networks (e.g.,
`Internet vs. Intranet) or user preference.
`
`We also plan to investigate new techniques for merging the results retrieved from each individual search engine.
`The implemented merging method ignores the actual distances between images; therefore more suitable images may be
`displayed as having less similarities. The information available in the database and user feedback may be used to refine the
`way the result images are displayed to the user.
`
`The MetaSEEk meta-search engine can be further improved by adding capabilities such as a support for customized
`search. QBIC and VisualSEEk allow the user to customize the search by graphically/manually specifying visual sketches as
`query input. The customized search on these two systems is supported for color percentages and color layout to allow the
`user to manually specify the amounts of different colors, or to specify different color locations, respectively. A customized
`search would require additional user interface programming and is not currently supported by MetaSEEk.
`
`As MetaSEEk allows image retrieval by entering keywords, we are also considering the possibility of keeping
`another database with search engine performance records for keyword search. Our goal would still be to investigate novel
`and efficient techniques for enhancing the user's interoperability with distributed VIR systems.
`
`6. REFERENCES
`
`[1] Daniel Dreilinger and Adele E. Howe, "Experiences with Selecting Search Engines Using Meta-search", to appear in
`ACM Transactions of Information Systems, 1997.
`
`[2J Shih-Fu Chang, John R. Smith, Mandis Beigi and Ana B. Benitez, "Visual Information Retrieval from Large Distributed
`On-line Repositories", to appear in Communications of ACM, 1997.
`
`127
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-10
`
`

`

`[3] Myron Flickner, Harpreet Sawhney, Wayne Niblack, Jonathan Ashley, Qian Huang, Byron Dom, Monika Gorkani, Jim
`Hafner, Denis Lee, Dragutin Petkovic, David Steele, and Peter Yanker, "Query by Image and Video Content: The QBIC
`System," IEEE Computer Magazine, Vol.28, No.9, pp. 23-32, 1995.
`
`[4] J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R.C. Jam and C. Shu, "Virage image search
`engine: an open framework for image management' Symposium on Electronic Imaging: Science and Technology --Storage
`& Retrievalfor Image and Video Databases IV, IS&T/SPIE'96, Feb. 1996.
`
`[5] J. R. Smith and S.-F. Chang, "VisualSEEk: A Fully Automated Content-Based Image Query System" ACM Multimedia
`Conference, Boston, MA, Nov. 1996. (WWW: http:llwww.ctr.columbia.edu/VisualSEEk, ftp: llftp.ctr.columbia.eduICTR-
`Research/advent/public/papers/96/smith96f.ps)
`
`[6] Luis Gavarno, Hector Garcia-Molina, and Anthony Tomasic, "The Effectiveness of Gloss for the Text Database
`Discovery Problems", Proceedings ofthe ACM, SIGMOD'94, Minneapolis, May 1994.
`
`[Ti Mic Bowman, Peter B. Danzig, Udi Manber, Michael F. Schwartz, Darren R. Hardy, and Duane P. Wessels. "Harvest: A
`scalable, customizable discovery and access system", Technical report, University of Colorado-Boulder, 1995.
`
`[8J B. Kahle and A. Medlar, "An Information System for Corporate Users: Wide Area Information Server", ConneXions —
`The Interoperability Report, Vol. 5, No. 1 1 ,pp. 2-9, Nov 1991.
`
`[9] Erik Selberg and Oren Etsioni, "Multi-service search and comparison using the MetaCrawler", Proceedings of the 4"
`International World Wide Web Conference, Dec 1995.
`
`[10] Center for Research, Inc., University of Kansas. ProFusion meta-search engine.
`http://www.designlab.ukans.edu/profusion/
`
`[11] H. Tamura and S. Mon and T. Yamawaki, "Textural Features Corresponding to Visual Perception", IEEE Trans. Syst.,
`Man, Cybern, Vol. 8, No. 6, 1978.
`
`128
`
`AMERICAN EXPRESS v. METASEARCH
`CBM2014-00001 EXHIBIT 2012-11
`
`