Image Retrieval Using the Color Approximation Histogram Based on Rough Set Theory

As a low-level feature in content-based image retrieval (CBIR), color histogram does not take into account the spatial correlation of the same or similar valued elements. In order to overcome this drawback, color approximation histogram based on rough sets theory is proposed in this paper. The image is partitioned into a collection of non-overlapping windows (called granule G). According to the pixels color in granule, color lower approximation histogram and color boundary histogram are denoted as low level feature in CBIR. Experiment results show that the precision and recall rate of color approximation histogram as low-level feature are higher than that of color histogram as low-level feature. The color approximation histogram classifies the granule into color lower approximation set or color boundary set, so it overcomes the drawback of color histogram as low-level feature. KeywordsRough set theory; CBIR; Color quantization; Color approximation histogram I. INTRODUCITON With the development of computer vision technology and the increase in the number of images taken by digital video devices, searching for images containing user-specified characteristics in large image databases has become more and more important and challenging than ever before. Image retrieval systems attempt to search through a database to find images that are perceptually similar to a query image. There are two frameworks: text-based and content-based. Text-based image retrieval is based on one or more keywords specified by the user to search image. However, there are cases in which a query request cannot be easily described by keywords. To overcome the above disadvantages in text-based retrieval system, content-based image retrieval (CBIR) was introduced in the early 1980s [1]. In CBIR, images are indexed by their visual content, such as color, texture, shapes and color layout (both color features and spatial relations). The features are stored in an image feature database for future use. When a query image is given, the features of the query image are extracted to match the features in the feature database by a pre-established algorithm, so that a group of similar images to the query image can be returned as the retrieval images. Color feature is one of the most widely used features in image retrieval. Colors are defined on a selected color space such as RGB, LAB, LUV, HSV (HSL), YCrCb and the huemin-max-difference (HMMD). Common color features or descriptors in CBIR systems include color-covariance matrix, color histogram, color moments, and color coherence vector [2]. As a content feature, a color spectrum or histogram is a simple and efficient low-level feature. However, if calculated directly in a triple-dimension color space (e.g., RGB), both the storage space and computing time will be very high. So color quantization is necessary before extraction of the image color features. In [3], quantized color histogram in HSV color space is selected as the feature to search image. The advantage of such method is that they do not need any prior information of the image. This method is based only on color levels and does not take into account the spatial correlation of the same or similar valued elements. In this paper, we proposed a new scheme: firstly, quantize the color in HSV color space, and map triple-color components into a one-dimension vector. Then partition image into a collection of non-overlapping windows (called granule G) [4], denote color lower approximation histogram and color boundary histogram based on rough sets theory as the color feature to retrieval images. This paper is organized as follows: In section , a brief overview of rough set is introduced. In section , feature extraction is presented. Experiments and results are discussed in Section . Finally conclusions are introduced in section . II. ROUGH SET THEORY PRELIMINARIES According to the definition given by Pawlak [5], an information system is a pair S=<U, A>, where U is a nonempty finite set of N objects {x1, x2,. , xN} called the universe, Education Department of Henan Province, China (2009B520013) 978-1-4244-4994-1/09/$25.00 ©2009 IEEE A is the set of values of attribute a. Supposing A B ⊆ and U X ⊆ , we can approximate the set X using only the information contained in B by constructing the lower and upper approximations of X. The lower and upper approximations can be defined as follows: [ ] { } Φ ≠ ∩ ∈ = X x U x X B B : (1) [ ] { } X x U x X B B ⊆ ∈ = : (2) Where [x] B denotes the equivalence class of the object x U relative to IB (the equivalence relation). The objects in X B can be certainly classified as members of X on the basis of knowledge in B, while objects in X B can only be classified as possible members of X on the basis of B. Obviously, the difference set yields the set of elements which lie around the boundary. The set ( ) X B X B X BN R − = is called the B-boundary of X, whose elements can not be classified to X or to –X using the knowledge B. The roughness of a set X with respect to B can be characterized numerically as ( ) X B X B X B − = 1 α (3) If roughness of the set X is 0 then X is crisp with respect to B, and if B α > 0 then X is rough. III. FEATHURE EXTRACTION This section presents the proposed algorithm for extracting color approximation histogram and the distance measure of two color approximation histograms. A. HSV color space HSV color space is one of important color spaces that separate out the luminance component (Intensity) of a pixel color from its chrominance components (Hue and Saturation). Hue represents pure color, which is perceived when incident light is of sufficient illumination and contains a single wavelength. Saturation gives a measure of the degree by which a pure color is diluted by white light. For light with low illumination, corresponding intensity value in the HSV color space is also low. HSV are defined mathematically by transformations between the r, g, and b color coordinates in the RGB model and the h, s, and v coordinates in the HSV models. The conversion is as follows. Let r, g, b [0, 1] be the red, green, and blue coordinates, respectively, of a color in RGB space. Accordingly, h [0,360], s, v [0, 1] are the hue, saturation and intensity coordinates, respectively, of a color in HSV space. The transformation is shown in (4) ~ (8) [6]. ( ) b g r v , , max = (4) ( ) v b g r v s , , min − = (5) ( ) ( ) ( ) b g r v b v b b g r v g v g b g r v r v r , , min , , min , , min