Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> Textural features corresponding to human visual perception are very useful for optimum feature selection and texture analyzer design. We approximated in computational form six basic textural features, namely, coarseness, contrast, directionality, line-likeness, regularity, and roughness. In comparison with psychological measurements for human subjects, the computational measures gave good correspondences in rank correlation of 16 typical texture patterns. Similarity measurements using these features were attempted. The discrepancies between human vision and computerized techniques that we encountered in this study indicate fundamental problems in digital analysis of textures. Some of them could be overcome by analyzing their causes and using more sophisticated techniques. <s> BIB001 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. > <s> BIB002 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> 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. <s> BIB003 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> MPEG-7 standardization work has started with the aims of providing fundamental tools for describing multimedia contents. MPEG-7 defines the syntax and semantics of descriptors and description schemes so that they may be used as fundamental tools for multimedia content description. In this paper, we introduce a texture based image description and retrieval method, which is adopted as the homogeneous texture descriptor in the visual part of the MPEG7 final committee draft. The current MPEG-7 homogeneous texture descriptor consists of the mean, the standard deviation value of an image, energy, and energy deviation values of Fourier transform of the image. These are extracted from partitioned frequency channels based on the human visual system (HVS). For reliable extraction of the texture descriptor, Radon transformation is employed. This is suitable for HVS behavior. We also introduce various matching methods; for example, intensity-invariant, rotation-invariant and/or scale-invariant matching. This technique retrieves relevant texture images when the user gives a querying texture image. In order to show the promising performance of the texture descriptor, we take the experimental results with the MPEG-7 test sets. Experimental results show that the MPEG-7 texture descriptor gives an efficient and effective retrieval rate. Furthermore, it gives fast feature extraction time for constructing the texture descriptor. <s> BIB004 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection. <s> BIB005 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> The existing local binary pattern (LBP) operators have three disadvantages: (1) They produce rather long histograms, which slow down the recognition speed especially on large-scale face database; (2) Under some certain circumstance, they miss the local structure as they don't consider the effect of the center pixel; (3) The binary data produced by them are sensitive to noise. Aiming at these problems, we proposed centralized binary pattern (CBP) operator. CBP operator has several advantages: (1) It reduces significantly the histograms' dimensionality by comparing pairs of neighbors in the operator; (2) It considers the center pixel point's effect and gives it the largest weight, thus improving discrimination; (3) By modifying the sign function of existing LBP operator, it decreases the white noise's influence on face images. Moreover, for the purpose of improving the robustness to small perturbation (deformation) of expressional images, we introduced image Euclidean distance (IMED) and embedded it in CBP. Experiments on two well-known facial expression databases demonstrate that the proposed method outperforms other modern approaches and show that IMED can enhance the performance of CBP in facial expression recognition. <s> BIB006 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions. <s> BIB007 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> The local binary pattern (LBP) operator is a computationally efficient local texture descriptor and has found many useful applications. However, its sensitivity to noise and the high dimensionality of histogram associated with a mediocre size neighborhood have raised some concerns. In this paper, we attempt to improve the original LBP by proposing a novel extension named extended local ternary pattern (ELTP). We will investigate the characteristics of ELTP in terms of noise sensitivity, discriminability and computational efficiency. Preliminary experimental results have shown better efficacy of ELTP over the original LBP. <s> BIB008 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data. <s> BIB009 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Texture Features <s> This paper presents a novel evaluationary approach to extract color-texture features for image retrieval application namely Color Directional Local Quinary Pattern (CDLQP). The proposed descriptor extracts the individual R, G and B channel wise directional edge information between reference pixel and its surrounding neighborhoods by computing its grey-level difference based on quinary value (?2, ?1, 0, 1, 2) instead of binary and ternary value in 0°, 45°, 90°, and 135° directions of an image which are not present in literature (LBP, LTP, CS-LBP, LTrPs, DExPs, etc.). To evaluate the retrieval performance of the proposed descriptor, two experiments have been conducted on Core-5000 and MIT-Color databases respectively. The retrieval performances of the proposed descriptor show a significant improvement as compared with standard local binary pattern LBP, center-symmetric local binary pattern (CS-LBP), Directional binary pattern (DBC) and other existing transform domain techniques in IR system. <s> BIB010
Like colors in the image, the textural characteristics are also effective ways of describing visual content. Texture features have also been widely used in CBIR applications. Textures can be quantified using three principal approaches. These are statistical, structural and spectral  . Statistical methods tend to define textures as smooth, coarse, fine, and grainy etc. Simple statistical descriptors include mean, moments, uniformity, correlation, contrast, homogeneity and entropy etc BIB009 . Structural methods deal with capturing the spatial arrangement of texture primitives in the image. Spectral techniques are derived by analyzing frequency spectrum of the image for global periodicity, high energy and narrow peaks etc  . In BIB001 the author proposed six texture features including directionality, coarseness, contrast, linelikeliness, regularity and roughness. The first three of these features were decided to be more important than the rest after experiments. The QBIC system BIB002 also uses the histograms of these features. Gabor features have also been used for texture analysis tasks. The responses from Gabor filter banks characterizes the textures. Texture features introduced in MPEG-7 are Texture Browsing Descriptor (TBD) BIB003 , Homogeneous Texture Descriptor (HTD) BIB004 and Edge Histogram Descriptor (EHD) BIB005 . The TBD captures texture characteristics like directionality, coarseness and regularity. It is computed from a set of band-pass filtered images. The filtered output is then used to compute components of the TBD. The descriptor is quite useful for browsing images. In combination with HTD, it can also be employed in retrieval applications with high accuracy. The HTD encodes quantitative characteristics of textures. This descriptor is calculated by applying a bank of filters on the image just like the TBD followed by computing the mean and standard deviation of the outputs in the frequency domain. It has been successfully applied for large scale texture retrieval purposes. The EHD represent textures by local edge characteristics. The spatial distribution of edges is a good texture representation useful for image to image matching even if their texture is not homogeneous. Its computation is fairly simple. The given image is sub-divided into 4 x 4 sub-images. Then local edge histograms for each of these sub-images is computed. Edges are categorized as vertical, horizontal, 45 diagonal, 135 diagonal and non-directional. Each of these type of edge contribute to a bin in the local edge histogram. The entire image gets divided into 16 partitions in which each partition has a five-bin histogram. Thus the full descriptor size is 80 bins. Most recently local texture features have been exploited for computing global description of the textures. One such method is called the local binary pattern (LBP)  . The LBP and its variants have been successfully applied to the task of texture classification. LBP is invariant to illumination changes in the image and since its inception it has been greatly improved. A large number of variants have been derived since then. It extracts information from the textures that is invariant to local grayscale changes in the image. It is calculated at each pixel position taking into account the values of the neighboring pixels forming small circular neighborhoods (with radius R pixels) around the value of a central pixel. The procedure for LBP computation is given in figure 3. The procedure is quite simple. If the value of the surrounding pixel is greater than the pixel value at the center, it produces '1' otherwise outputs '0'. The string of eights bits is converted into a decimal value and put in the position of the central pixel in the LBP map. A variant of LBP called Centralized Binary Pattern (CBP), proposed in BIB006 , compares pixel pairs in the locality to reduce the lengthy histogram, and it considers the local effect by assigning the largest weight to the central pixel. In another variant Completed LBP (CLBP)  utilizes both the magnitude and sign of the difference of the surrounding pixel and the central pixel in contrast to conventional LBP which only utilizes magnitude information. The LBP is a non-directional first order derivative operator. Local Derivative Pattern (LDP) BIB007 is another variant that uses higher order derivatives. Higher order derivatives capture direction information in the neighboring pixels. The local ternary pattern (LTP) BIB008 extends the LBP by using three values i.e. -1, 0 and 1 instead of binary values. In local quinary pattern (LQP) BIB010 five labels are used i.e. -2, -1, 0, 1, 2 based on the amount of difference in the central and surrounding pixel. LBP and its variants have proven to be powerful local descriptors. The extensive adaptation of these methods for a variety of tasks involving texture analysis is a proof of their strength. Hence these methods need to be explored in more detail.