Wavelet domain majority coupled binary pattern: a new descriptor for texture classification

In this paper, a new approach for texture classification called wavelet domain majority coupled binary pattern is proposed. Here, the single-level wavelet transform is applied which decomposes the image, resulting in wavelet coefficients. The wavelet coefficients present in all the four sub-bands are taken for further processing. The relationship of wavelet coefficients present at distances one, two and three is utilized. The average wavelet coefficients present at various distances are compared with the center wavelet coefficient of the local region, resulting in binary value. For each distance,  eight bit binary pattern is generated. Altogether, three distances yield three eight bit binary pattern. Then, the rule of majority is applied to the three  eight bit binary pattern and results in generation of proposed label. The proposed labels together contribute for the construction of histogram. Finally, the distance measure is used to identify the similarity between query and database images. Experimental results show that the proposed method achieves the average retrieval rate of 88.92% on Brodatz, 93.95% on Outex and 90.53% on Virus databases. This shows that the proposed method achieves good performance and outperforms other existing methods.