Comparative study of global color and texture descriptors for web image retrieval

This paper presents a comparative study of color and texture descriptors considering the Web as the environment of use. We take into account the diversity and large-scale aspects of the Web considering a large number of descriptors (24 color and 28 texture descriptors, including both traditional and recently proposed ones). The evaluation is made on two levels: a theoretical analysis in terms of algorithms complexities and an experimental comparison considering efficiency and effectiveness aspects. The experimental comparison contrasts the performances of the descriptors in small-scale datasets and in a large heterogeneous database containing more than 230 thousand images. Although there is a significant correlation between descriptors performances in the two settings, there are notable deviations, which must be taken into account when selecting the descriptors for large-scale tasks. An analysis of the correlation is provided for the best descriptors, which hints at the best opportunities of their use in combination.

► Theoretical comparison of 52 image descriptors by asymptotic complexity of algorithms. ► Experimental comparison of descriptors by efficiency and effectiveness criteria. ► Evaluation of descriptors on a large-scale Web scenario. ► Contraposition of performances on small- and large-scale scenarios. ► Correlation analysis of descriptors effectiveness for combination purposes.