Image retrieval by using texture and shape correlated hand crafted features.

Content-based image retrieval (CBIR) has become one of the trending areas of research in computer vision. In this paper, consonance on hue, saturation, and intensity is used by applying inter-channel voting between them. Diagonally symmetric pattern (DSP) from the intensity component of the image is computed. The grey level co-occurrence matrix (GLCM) is applied to DSP to extract texture features. Histogram of oriented gradients (HOG) features is used to extract the shape information. All three features are concatenated. To evaluate the efficiency of our method, five performance measures are calculated, i.e., average precision rate (APR), average recall rate (ARR), F-measure, average normalised modified retrieval rank (ANMRR) and total minimum retrieval epoch (TMRE). Corel-1K, Corel-5K, Corel-10K, VisTex, STex, and colour Brodatz are used. The experimental results show an improvement in 100% cases for Corel-1K dataset, 80% cases for Corel-5k and 80% cases for each of the three texture datasets.