Content-Based Image Retrieval Using a Combination of Texture and Color Features

Image retrieval is headed towards the ultimate goal of achieving the performance very similar to human cognitive ability. As an attempt of such work, this paper proposes a content-based image retrieval using a combination of texture features extracted from Gabor local correlation and uniform magnitude local binary pattern in value component and color features from color autocorrelogram in hue and saturation components. The texture features have multi-resolution multi-direction characteristics. In contrast, the color features have spatial structural information for color, which is rotation-invariant. Further, the HSV color space used herein is similar to the human visual system. Especially, two-dimensional (2D) Gabor transform used to extract parts of texture features, mimics the biological visual strategy of embedding angular and spectral analysis within global spatial coordinates, as using empirical 2D receptive field profiles obtained from orientation-selective neurons in cat visual cortex as the weighting functions. Based on the experimental results, we confirm that the proposed combined method outperforms compared existing methods and the methods using partial ones stemming from the proposed features in terms of retrieval performance.