Utilizing multiscale local binary pattern for content-based image retrieval

With the development of different image capturing devices, huge amount of complex images are being produced everyday. Easy access to such images requires proper arrangement and indexing of images which is a challenging task. The field of Content-Based Image Retrieval (CBIR) deals with finding solutions to such problems. This paper proposes a CBIR technique through multiscale Local Binary Pattern (LBP). Instead of considering consecutive neighbourhood pixels, Local Binary Pattern of different combinations of eight neighbourhood pixels is computed at multiple scales. The final feature vector is constructed through Gray Level Co-occurrence Matrix (GLCM). Advantage of the proposed multiscale LBP scheme is that it overcomes the limitations of single scale LBP and acts as more robust feature descriptor. It efficiently captures large scale dominant features of some textures which single scale LBP fails to do and also overcomes some of the limitations of other multiscale LBP techniques. Performance of the proposed technique is tested on five benchmark datasets, namely, Corel-1K, Olivia-2688, Corel-5K, Corel-10K, and GHIM-10K and measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms other multiscale LBP techniques as well as some of the other state-of-the-art CBIR methods.