Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Evaluation of MPEG-7 shape descriptors against other shape descriptors
Multimedia Systems
A search engine for historical manuscript images
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
WND-CHARM: Multi-purpose image classification using compound image transforms
Pattern Recognition Letters
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
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Shape descriptors have been used frequently as features to characterize an image for classification and image retrieval tasks. For example, the patent office uses the similarity of shape to ensure that there are no infringements of copyrighted trademarks. This paper focuses on using machine learning and information retrieval techniques to classify an image into one of many classes based on shape. In particular, we compare Support Vector Machines, Naïve Bayes and relevance language models for classification. Our results indicate that, on the MPEG-7 database, the relevance model outperforms the machine learning techniques and is competitive with prior work on shape based retrieval. We also show how the relevance model approach may be used to perform shape retrieval using keywords. Experiments on the MPEG-7 database and a binary version of the COIL-100 database show good retrieval performance.