DBC Co-occurrence Matrix for Texture Image Indexing and Retrieval

In this paper, a new image indexing and retrieval algorithm using multiresolution directional binary code (DBC) co-occurrence matrix is proposed. DBC histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by DBC Co-occurrence. Multi-resolution texture decomposition and co-occurrence calculation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of DBCs from these particular textures. Eventually, feature vectors are constructed by making into play the co-occurrence matrix that exists between binary patterns. The retrieval results of the proposed method have been tested by conducting experiment on Brodatz texture database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, DBC and other transform domain features.