Published in IET Image Processing Received on 14th September 2011 Revised on 7th July 2012 doi: 10.1049/iet-ipr.2011.0445 ISSN 1751-9659 Image retrieval and classification using adaptive local binary patterns based on texture features C.-H. Lin 1 C.-W. Liu 2 H.-Y. Chen 3 1 Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road, Taichung, Taiwan 2 Department of Computer and Information Science, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan 3 Department of Electrical Engineering, National Chung Hsing University, No. 250 Kuo Kuang Road, Taichung, Taiwan E-mail: linch@ntit.edu.tw Abstract: In this study, adaptive local binary patterns (ALBP) are proposed for image retrieval and classification. ALBP are based on texture features for local binary patterns. The texture features were used to propose an adaptive local binary patterns histogram (ALBPH) and gradient for adaptive local binary patterns (GALBP) in this study. Two texture features are most useful for describing the relationship in a local neighbourhood. ALBPH shows the texture distribution of an image by identifying and employing the difference between the centre pixel and the neighbourhood pixel values. In the GALBP, the gradient for each pixel is computed and the sum of the gradient of the ALBP number is adopted as an image feature. In this study, a set of colour and greyscale images were used to generate a variety of image subsets. Then, image retrieval and classification experiments were carried out for analysis and comparison with other methods. From the experimental results, the authors discovered that the proposed feature extraction method can effectively describe the characteristics of images in regard to texture image and image type. The image retrieval and classification experiments also produced better results than other methods. 1 Introduction In recent years, with the emergence of the internet, multimedia data can be more easily shared. These include digital images of textures, natural images, images of animals and plants, digital signs, fingerprint images, facial images, digital maps, medical images, art images and others. Large amounts of digital content have been created, stored and disseminated on account of the rapid expansion in image creation, storage and management technologies. The best method for effectively and efficiently retrieving desirable images from a constantly growing image database has thus become an important issue. Texture features in an image play an important role in computer vision and image processing. Image retrieval and classification based on texture features are the active research topics in the field of computer vision and pattern recognition [1]. This paper focuses on the building of an efficient and accurate texture image retrieval and classification system. Many texture feature-based image retrieval systems have been proposed in the academic arena [2–8]. The texture classification methods have been the focus of many studies [9–16]. Huang and Dai [2] proposed a texture-based image retrieval system integrated with both wavelet decomposition and a gradient vector. The system of Jhanwar et al. [3] is based on a motif co-occurrence matrix which converts the differences among pixels into a basic graphic and computes the probability of its occurrence in the adjacent area as a texture feature of an image. Hafiane and Zavidovique [6] focused on a novel description of coloured textures using local relational string (LRS) based on the relative relations between neighbouring pixels and their distribution. Lin et al.’s [8] approach is based on a colour co-occurrence matrix and uses the difference between pixels in scan patterns for the colour and texture image retrieval. For texture image classification, Deng and Clausi [11] developed an anisotropic circular Gaussian MRF model for retrieving rotation-invariant texture features. Varma and Zisserman [12] investigated texture classification using single images obtained from an unknown viewpoint and illumination. Bianconi et al. [14] proposed a system of coordinated clusters representation (CCR) based on the probability of occurrence in elementary binary patterns (texels) defined over a square window. The CCR was originally proposed for binary textures, but was later extended to greyscale texture images through global image thresholding. Ojala et al. [17] proposed the concept of a local-binary- pattern (LBP) operator. The LBP operator primarily describes the texture in images and provides a theoretically simple and multi-resolution statistical method. Many studies also discuss an LBP operator [17–27]. An LBP operator is an effective way to describe image texture features. More recently, an LBP operator has been used in other applications such as classification [16–22], facial 822 IET Image Process., 2012, Vol. 6, Iss. 7, pp. 822–830 & The Institution of Engineering and Technology 2012 doi: 10.1049/iet-ipr.2011.0445 www.ietdl.org