Automated binary texture feature sets for image retrieval

Digital image and video libraries require new algorithms for the automated extraction and indexing of salient image features. Texture features provide one important cue for the visual perception and discrimination of image content. We propose a new approach for automated content extraction that allows for efficient database searching using texture features. The algorithm automatically extracts texture regions from image spatial-frequency data which are represented by binary texture feature vectors. We demonstrate that the binary texture features provide excellent performance in image query response time while providing highly effective texture discriminability, accuracy in spatial localization and capability for extraction from compressed data representations. We present the binary texture feature extraction and indexing technique and examine searching by texture on a database of 500 images.