Design and evaluation of algorithms for image retrieval by spatial similarity
ACM Transactions on Information Systems (TOIS)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Color Indexing Using Rotation, Translation, and Scale Invariant Anglograms
Multimedia Tools and Applications
Semantic facets: an in-depth analysis of a semantic image retrieval system
Proceedings of the 6th ACM international conference on Image and video retrieval
Activity based surveillance video content modelling
Pattern Recognition
Classification and Automatic Annotation Extension of Images Using Bayesian Network
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Modeling, classifying and annotating weakly annotated images using Bayesian network
Journal of Visual Communication and Image Representation
Performing content-based retrieval of humans using gait biometrics
Multimedia Tools and Applications
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In this paper, we present the results of our work that seeks to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords, sometimes, called concepts, so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based web document retrieval, using both keywords and image features to represent the documents.