Image content search by color and texture properties

Abstract

A new scheme for color and texture feature extraction for image content search is presented. We introduce a scheme using the two chrominance components for color information and a computationally efficient infinite impulse response (IIR) quadrature mirror filter bank (QMF) energy measure of the luminance component for texture information. The color and texture information is combined into one feature vector, and the components are balanced with respect to dimensionality. We illustrate the utility of our features with experiments in searching for a specific color texture in a large database of images. Several different sub-band decompositions are evaluated. Features extracted using a previously published Gabor filter bank are also evaluated against the proposed scheme. We conclude that the proposed scheme outperforms the Gabor features in both quality and complexity.