Image retrieval by hypertext links
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Web mining for web image retrieval
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
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IEEE Transactions on Knowledge and Data Engineering
Discovering informative content blocks from Web documents
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic image annotation by mining the web
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Web query expansion by wordnet
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Web image retrieval refinement by visual contents
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
Multi-term web query expansion using wordnet
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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This paper presents our implementation techniques for an intelligent Web image search engine. A reference architecture of the system is provided and addressed in this paper. The system includes several components such as a crawler, a preprocessor, a semantic extractor, an indexer, a knowledge learner and a query engine. The crawler traverses web sites in multithread accesses model. And it can dynamically control its access load to a Web server based on the corresponding capacity of the local system. The preprocessor is used to clean and normalize the information resource downloaded from Web sites. In this process, stop-word removing and word stemming are applied to the raw resources. The semantic extractor derives Web image semantics by partitioning combining the associated text. The indexer of the system creates and maintains inverted indices with relational model. Our knowledge learner is designed to automatically acquire knowledge from users' query activities. Finally, the query engine delivers search results in two phases in order to mine out the users' feedbacks.