Abstract:
In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our
proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the
HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of bins and
performed the voting with saturation values and vice versa by following a principle similar to that of the HOG descriptor,
where orientation of the gradient is quantized into a certain number of bins and voting is done with gradient magnitude. This
helps us to study the nature of variation of saturation with variation in Hue and nature of variation of Hue with the variation
in saturation. The texture component of our descriptor considers the co-occurrence relationship between the pixels symmetric
about both the diagonals of a 3×3 window. Our work is inspired from the work done by Dubey et al. (IEEE Signal Process
Lett 22(9):1215–1219, [2015]). These two components, viz. color and texture information individually perform better than
existing texture and color descriptors. Moreover, when concatenated the proposed descriptors provide a signifcant improvement over existing descriptors for content base color image retrieval. The proposed descriptor has been tested for image
retrieval on fve databases, including texture image databases—MIT-VisTex database and Salzburg texture database and
natural scene databases Corel 1K, Corel 5K and Corel 10K. The precision and recall values experimented on these databases
are compared with some state-of-art local patterns. The proposed method provided satisfactory results from the experimen
