Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix

Abstract

In this paper, two extended versions of motif co-occurrence matrices (MCM) are derived and concatenated for efficient content-based image retrieval (CBIR). This paper divides the image into 2 x 2 grids. Each 2 x 2 grid is replaced with two different Peano scan motif (PSM) indexes, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. This transforms the entire image into two different images and co-occurrence matrices are derived on these two transformed images: the first one is named as "motif co-occurrence matrix initiated from top left most pixel (MCM TL )" and second one is named as "motif cooccurrence matrix initiated from bottom right most pixel (MCM BR )". The proposed method concatenates the feature vectors of MCM TL and MCM BR and derives multi motif co-occurrence matrix (MMCM) features. This paper carried out investigation on image databases i.e. Corel-1k, Corel-10k, MIT-VisTex, Brodtaz, and CMU-PIE and the results are compared with other well-known CBIR methods. The results indicate the efficacy of the proposed MMCM than the other methods and especially on MCM [19] method.