Unifying textual and visual cues for content-based image retrieval on the World Wide Web
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Bayesian Relevance Feedback for Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Image Retrieval Using Multiple Evidence Ranking
IEEE Transactions on Knowledge and Data Engineering
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Clustering and searching WWW images using link and page layout analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Webpage segmentation for extracting images and their surrounding contextual information
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Multifaceted conceptual image indexing on the world wide web
Information Processing and Management: an International Journal
CISC: clustered image search by conceptualization
Proceedings of the 16th International Conference on Extending Database Technology
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Images on the Web appear with textual contents providing meaningful information to their semantics. Methods that automatically determine and extract the Web image context from an HTML document are widely used in different applications. However, the performance of the image context extraction has rather been evaluated on its own. Keeping this imperative in mind, we present a framework to objective evaluation and comparison of the performance of image context extraction methods. This is achieved by collecting a large ground truth dataset consisting of diverse Web documents from real Web servers and by defining performance measures adapted to fit the special properties of the context extraction task. To show the capabilities of the proposed framework, common extraction methods from the literature have been evaluated and the results are summarized in this paper.