Wavelet domain majority coupled binary pattern: a new descriptor for texture classification
- PDF / 2,351,025 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 88 Downloads / 175 Views
INDUSTRIAL AND COMMERCIAL APPLICATION
Wavelet domain majority coupled binary pattern: a new descriptor for texture classification S. Nithya1 · S. Ramakrishnan1 Received: 10 May 2019 / Accepted: 5 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Texture classification · Local binary pattern · Texture feature extraction
1 Introduction The wide availability of digital images nowadays paves a way forward for extraction of digital images from a pool of images using texture classification. Nowadays, the texture classification has been gaining attention due to wide usability of digital images in various applications such as sign language recognition [1], object recognition [2], X-ray image classification [3] and facial gender classification [4]. The techniques available for extracting texture features are wavelet transform [5, 6], Gabor filters [7] and local binary pattern (LBP) [8]. The discrete wavelet transform (DWT) [9] extracts the salient texture feature of an image effectively. The texture retrieval using Kullback–Leibler distance in wavelet domain [10] is proposed. Arivazhagan et al. [11] introduced texture * S. Nithya [email protected] S. Ramakrishnan [email protected] 1
Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
classification involving Gabor filters for rotation invariant. Kokare et al. [12] suggested a technique involving rotated wavelet filters for texture classification. Novel rotated complex wavelet filters [13] are proposed for texture feature extraction. Hafiane et al. [14] presented median binary pattern for texture classification of images. The LBP, texture descriptor, has been extensively applied in various pattern recognition ap
Data Loading...
