Learning shapes for image classification and retrieval

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, Naive 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.