Content-based image retrieval using multi-scale averaging local binary patterns

Abstract

Feature extraction has a significant impact on the accuracy of Content-Based Image Retrieval (CBIR) algorithms since the content of images is encoded in feature vectors. In this paper, an effective method for texture feature extraction is proposed based on local patterns. In the proposed method, first the image is formed in different scales, then the texture features are extracted from the scales of the image. Finally, extracted features of different scales are concatenated to construct the final feature vector. To evaluate the proposed method, five datasets including Corel-1k, Brodatz, VisTex, Corel-10k, STex, Caltech256, and Oliva are used. In the evaluation process, the effect of different scales and different coding schemes on retrieval precision is investigated. The proposed method is compared with existing CBIR models based on local patterns. The proposed method achieves the best precision of 64.16%, 81.66%, 88.59%, 30.63%, and 69.63%, 13.59%, and 64.10% on the Corel-1k, Brodatz, VisTex, Corel-10k, STex, Caltech256, and Oliva datasets, respectively.