Color Texture Classification by Normalized Color Space Representation Constantin Vertan, Nozha Boujemaa INRIA Rocquancourt – Projet IMEDIA Domaine de Voluceau BP105 Rocquancourt 78153 Le Chesnay Cedex, France Constantin.Vertan,
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Abstract This paper proposes a novel approach to color texture characterization and classification. Rather than developing new textural features, we propose to derive a family of new, reduced dimensionality color spaces (that we call ), that allow a good classification performance by the use of classical energy-distribution features, defined in a scalar spectral domain. The dimensionality reduction approach can be traced back to color constancy normalization and the reduced ordering principle and exhibits a strong perceptual background. We develop an adaption procedure for the selection of the proper color space within the new family. The overall classification performance is very promising and the proposed methodology surmounts the current color texture characterization by energetic features extracted from the luminance spectrum only.
1. Introduction The ever increasing range of computer vision applications continuously fed the need for faster and more accurate image analysis algorithms. The field of texture description, recognition and classification presents the same evolution: more and more description methods are reported, getting the benefits from the latest signal processing and mathematical tools (such as the multi-resolution or scale-space approaches) [5], [11]. The basic assumption is that the energy distribution in the frequency domain identifies a texture. Hence, if the frequency spectrum of a textured image is decomposed into a sufficient number of sub-bands, the spectral signature of different texture will be different enough to insure an accurate classification. Rather than developing new textural features, we propose to derive a family of new, reduced dimensionality color spaces (that we ), that allow a good classificawill subsequently call tion performance by the use of classical (scalar type) features, defined in the spectral domain.
2. Representing Colors Since the early beginnings of color science, the problem of color representation was intensively studied. Its basis were set by Maxwell, who showed that any color can be matched by a mixture of properly weighted primary stimuli. Later referred as the trichromaticity principle and supported by physiological and anatomical studies, the representation of the colors as triples is generally used [7].
2.1. Trichromatic representations The color space is the most frequently used color representation method in image processing [7], [1]. Its limitations imposed the development of many derived classes of color representation. The basis for their definition are the perceptual motivation, the uniform chromaticity scaling, the linear transformation of the or a combination of them. The selection of a color space is crucial to any color object related problem. The family of (Hue - Saturation - Value) color spaces is the typical paradigm of perceptualbased color description, and its use in image segmentation provided significant results. The and [7], [1] color spaces are used for their capability of representing perceived color difference through Euclidean distances. The opponent color space [1], [8] is a linear transformation of that matches the physiology of the human visual system. The three coordinates are a achromatic (luminance) value and two chromatic coordinates, given by the green–red and yellow–blue differences (1). Providing a separation of the color and intensity informations, it proved its success in color image indexing [8], [1].
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A similar chromaticity information separation is achieved by the Ohta color space [1] using a statistical study of the uncorrelated color components on a large population of typical images. The transform is given by (2).
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3 %$4 4$& 6587:9 3 ,+; 65 distance ( 3NY + 5 the image and thus select the proper chromatic space. Take for instance the case of an image with the blue component being the least significant (at least from a perceptual point of view, according to the perception experiments); representation the color space then we will use for its derived from the opponent color theory (6). Several procedures are at hand for the identification of the least significant color component. We may use the range, the variance, the eigenvalues or some normalized scalars, such as the contrast ratio (variance to average ratio) or the range to average ratio. We used the later, due to its reasonable tradeoff between computing complexity and precision.
4. Experiments The experiments were conducted on two texture databases; a first database is formed by 56 classes of fairly regular textures, with 9 examples for each texture class; the second database consists of 32 classes of irregular textures (mostly painting details or natural scenes), with 10 examples for each texture class. Within the same class we have differences in illumination, camera position, scale. A third database was formed by all the available textures, with 9 examples for each class. The color texture images are extracted from the Vistex data base at MIT Vision and Modeling Group. We characterize the spectral energy distribution of a texture spectrum by the standard method of computing the relative amount of energy ced in different spectral regions fbd (either concentric circular disks, either circular sectors) of the energy spectrum 3Ng 5.
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