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Texture descriptors such as local binary patterns (LBP) have been successfully employed for feature extraction in image retrieval algorithms because of their high discriminating ability and computational efficiency. In this paper, we propose histogram feature refinement methods for enhancing performance of texture descriptor based content-based image retrieval (CBIR) systems. In the proposed approach for histogram refinement, each pixel in the query and database images is classified into one of the two categories based on the analysis of pixel values in its neighborhood. Local patterns corresponding to two sets of pixels are used to generate two histogram features for each image, effectively resulting in splitting of the original global histogram of texture descriptors into two based on the category of each pixel. Resulting histograms are then concatenated to form a single histogram feature. This study also explores three hybrid frameworks for histogram refinement in CBIR systems. Comparison of histogram features corresponding to query and database images are performed using the relative l1 distance metric. Performance evaluation on three publicly available benchmark image databases namely, GHIM 10000, COREL 1000 database, and Brodatz texture database shows that performances of existing texture descriptor based approaches improve considerably when the proposed histogram feature refinement is incorporated. Specifically, the average precision rate is improved by 6.02%, 5.69%, 4.79%, and 4.21% for LBP, local derivative pattern (LDP), local ternary pattern (LTP), and local tetra pattern (LTrP) descriptors, respectively on GHIM 10000 database. The proposed histogram refinement approaches also provide performance improvement for other texture descriptors considered in this study. A general framework for histogram refinement of texture descriptors.Improves retrieval performance of texture descriptor based CBIR systems.Performance improvement in CBIR validated using 9 texture descriptors.Marginal increase in average image retrieval time with 1.2s in the worst case.

A good image representation is critical to face recognition task. Recently, eight-direction Kirsch masks based image descriptors, e.g. local directional pattern (LDP), local sign directional pattern (LSDP), have been devised and shown competitive results than conventional LBP descriptor. However, these methods may lose or do not fully explore valuable texture information of the image. To remedy this drawback, a novel discriminative image descriptor, namely local edge direction and texture descriptor (LEDTD) is proposed in this paper. LEDTD differs from the existing Kirsch based methods in a manner that it not only considers image edge direction information but also extracts image texture feature by encoding the edge response directions of center and its neighborhood pixels by employing local XOR binary coding strategy. Finally, edge direction and texture features are integrated to form the image feature vector. Extensive performance evaluations on four benchmark face databases show that the proposed approach yields a better performance in terms of the recognition rate as well as robustness to the noise compared with the state of the art methods.

In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference ( BIGD ). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and gradient differences of pairwise blocks to construct the local BIGD descriptor. The random sampling strategy and the multi-scale framework help BIGD descriptors capture the distinctive patterns of patches at different orientations and spatial granularity levels. We use vectors of locally aggregated descriptors (VLAD) or improved Fisher vector (IFV) to encode local BIGD descriptors into a full image descriptor, which is then fed into a linear support vector machine (SVM) classifier for texture classification. We compare the proposed descriptor with typical and state-of-the-art ones by evaluating their classification performance on five public texture data sets including Brodatz, CUReT, KTH-TIPS, and KTH-TIPS-2a and -2b. Experimental results show that the proposed BIGD descriptor with stronger discriminative power yields 0 . 12 % ∼ 6 . 43 % higher classification accuracy than the state-of-the-art texture descriptor, dense microblock difference (DMD).

Gabor filtering is a widely applied approach for texture analysis. This technique shows a strong dependence on certain number of parameters. Unfortunately, each variation of values of any parameter may affect the texture characterization performance. Moreover, Gabor filters are unable to extract micro-texture features which also have a negative effect on the clustering task. This paper, deals with a new descriptor which avoids the drawbacks mentioned above. The novel texture descriptor combines grating cell operator outputs derived from a designed Gabor filters bank, and local binary pattern features. For the clustering task, an extended version of fuzzy type 2 clustering algorithm is also proposed. The effectiveness of the proposed segmentation approach on a variety of synthetic and textured images is highlighted. Several experimental results on a set of textured images show the superiority of the proposed approach in terms of segmentation accuracy with respect to quantitative and qualitative comparisons.

Image texture is useful in image browsing, search and retrieval. A texture descriptor based on a multiresolution decomposition using Gabor wavelets is proposed. The descriptor consists of two parts: a perceptual browsing component (PBC) and a similarity retrieval component (SRC). The extraction methods of both PBC and SRC are based on a multiresolution decomposition using Gabor wavelets. PBC provides a quantitative characterization of the texture’s structuredness and directionality for browsing application, and the SRC characterizes the distribution of texture energy in di!erent subbands, and supports similarity retrieval. This representation is quite robust to illumination variations and compares favorably with other texture descriptors for similarity retrieval. Experimental results are provided. ( 2000 Elsevier Science B.V. All rights reserved.