Evaluating the Performance of Content-Based Image Retrieval Systems

Content-based image retrieval (CBIR) is a new but in recent years widely-adopted method for finding images from vast and unannotated image databases. CBIR is a technique for querying images on the basis of automatically-derived features such as color, texture, and shape directly from the visual content of images. For the development of effective image retrieval applications, one of the most urgent issues is to have widely-accepted performance assessment methods for different features and approaches. In this paper, we present methods for evaluating the retrieval performance of different features and existing CBIR systems. In addition, we present a set of retrieval performance experiments carried out with an experimental image retrieval system and a large database of images from a widely-available commercial image collection.

[1]  Alexander Dimai Assessment of Effectiveness of Content Based Image Retrieval Systems , 1999, VISUAL.

[2]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[3]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[4]  Pasi Koikkalainen,et al.  Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[5]  Erkki Oja,et al.  Content-Based Image Retrieval Using Self-Organizing Maps , 1999, VISUAL.

[6]  Erkki Oja,et al.  Self-Organizing Maps for Content-Based Image Database Retrieval , 1999 .

[7]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[8]  Ingemar J. Cox,et al.  Target testing and the PicHunter Bayesian multimedia retrieval system , 1996, Proceedings of the Third Forum on Research and Technology Advances in Digital Libraries,.