Making colors worth more than a thousand words
Proceedings of the 2008 ACM symposium on Applied computing
Directional Hartley transform and content based image retrieval
Signal Processing
A framework towards a multi-modal fingerprinting scheme for multimedia assets
International Journal of Business Information Systems
Automatic image search based on improved feature descriptors and decision tree
Integrated Computer-Aided Engineering
Classification of tuberculosis digital images using hybrid evolutionary extreme learning machines
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Content-based image retrieval (CBIR) is an important research area for manipulating large amount of image databases and archives. Extraction of invariant features is the basis of CBIR. This paper focuses on the problem of texture and shape feature extractions. We investigate texture feature and shape feature for CBIR by successfully combining the Gabor filters and Zernike moments (GF+ZM). GF is used for texture feature extraction and ZM extracts shape features. Comprehensive performance evaluation of our method is based on three different databases: face database, fingerprint database, and MPEG-7 shape database. The experimental results demonstrate that GF+ZM presents robustness to all of the three databases with the best average retrieval rate while the GF and ZM are limited for certain databases. GF is effective for face database and fingerprint database but is weak for MPEG-7 shape database. ZM achieves high retrieval rate for face database and MPEG-7 shape database but gives relatively low retrieval rate for fingerprint database.