Enhanced Gabor wavelet correlogram feature for image indexing and retrieval

Abstract

In this paper, a new feature scheme called enhanced Gabor wavelet correlogram (EGWC) is proposed for image indexing and retrieval. EGWC uses Gabor wavelets to decompose the image into different scales and orientations. The Gabor wavelet coefficients are then quantized using optimized quantization thresholds. In the next step, the autocorrelogram of the quantized wavelet coefficients is computed in each wavelet scale and orientation. Finally, the EGWC index vector simply consists of the autocorrelogram coefficients. Due to non-orthogonality of Gabor decomposition, the resulting wavelet coefficients suffer from redundancy, which increases the computational cost and reduces the effectiveness of EGWC. Here, we present a solution to handle the redundancy problem using non-maximum suppression and adjustment of autocorrelogram distance parameters as a function of the wavelet scale. The retrieval results obtained by applying EGWC to index two image databases with 5,000 natural images and 1,792 texture images demonstrated its better performance in terms of retrieval rates with respect to the state-of-the-art content-based and multidirectional texture indexing algorithms.