The extraction of high-level color descriptors is an increasingly important problem, as these descriptions often provide links to image content. When combined with image segmentation, color naming ...can be used to select objects by color, describe the appearance of the image, and generate semantic annotations. This paper presents a computational model for color categorization and naming and extraction of color composition. In this paper, we start from the National Bureau of Standards' recommendation for color names, and through subjective experiments, we develop our color vocabulary and syntax. To assign a color name from the vocabulary to an arbitrary input color, we then design a perceptually based color-naming metric. The proposed algorithm follows relevant neurophysiological findings and studies on human color categorization. Finally, we extend the algorithm and develop a scheme for extracting the color composition of a complex image. According to our results, the proposed method identifies known color regions in different color spaces accurately, the color names assigned to randomly selected colors agree with human judgments, and the description of the color composition of complex scenes is consistent with human observations.
We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each ...segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.
Color descriptors are among the most important features used in image analysis and retrieval. Due to its compact representation and low complexity, direct histogram comparison is a commonly used ...technique for measuring the color similarity. However, it has many serious drawbacks, including a high degree of dependency on color codebook design, sensitivity to quantization boundaries, and inefficiency in representing images with few dominant colors. In this paper, we present a new algorithm for color matching that models behavior of the human visual system in capturing color appearance of an image. We first develop a new method for color codebook design in the Lab space. The method is well suited for creating small fixed color codebooks; for image analysis, matching, and retrieval. Then we introduce a statistical technique to extract perceptually relevant colors. We also propose a new color distance measure that is based on the optimal mapping between two sets of color components representing two images. Experiments comparing the new algorithm to some existing techniques show that these novel elements lead to better match to human perception in judging image similarity in terms of color composition.
Although most of the theoretical and implementation aspects of wavelet based algorithms in texture characterization are well studied and understood, many issues related to the choice of filter bank ...in texture processing remain unresolved. The impact of the wavelet basis has been mentioned in a few papers, whereas other more detailed investigations have considered only the choice of the wavelet basis in image coding. For example, regularity, number of vanishing moments and a shift variance degree have been used for the filter evaluation in image coding, but the utility of all these metrics in texture analysis has not yet been established. Therefore, the scope of this paper was to investigate whether the properties of decomposition filters play an important role in texture description, and which feature is dominant in the selection of an optimal filter bank. We performed classification experiments with 23 Brodatz textures. Our investigation shows that the selection of the decomposition filters has a significant influence on the result of texture characterization. Finally, the paper ranks 19 orthogonal and biorthogonal filters, and establishes the most relevant criteria for choice of decomposition filters in wavelet-based texture characterization algorithms.
We propose a method for semantic categorization and retrieval of photographic images based on low-level image descriptors derived from perceptual experiments. The method applies multidimensional ...scaling and hierarchical clustering analysis to identify candidate semantic categories into which human observers organize images. Through a series of subjective experiments we refine our definition of these categories and select a set of low-level image features that uniquely describe them. We then devise a new image similarity metric and develop a prototype system, which identifies the semantic category of the image and retrieves similar images from the database. We tested the metric on a set of new images and compared the categorization results with that of human observers. Our results provide a good match to human performance, thus validating the use of human judgments to develop semantic descriptors. Our method can be used for the enhancement of current retrieval methods, better organization of image/video databases, and the development of more intuitive navigation schemes, browsing methods and user interfaces.
Color quantization is sampling of three-dimensional (3-D) color spaces (such as RGB or Lab) which results in a discrete subset of colors known as a color codebook or palette. It is extensively used ...for display, transfer, and storage of natural images in Internet-based applications, computer graphics, and animation. We propose a sampling scheme which provides a uniform quantization of the Lab space. The idea is based on several results from number theory and phyllotaxy. The sampling algorithm is very much systematic and allows easy design of universal (image-independent) color codebooks for a given set of parameters. The codebook structure allows fast quantization and ordered dither of color images. The display quality of images quantized by the proposed color codebooks is comparable with that of image-dependent quantizers. Most importantly, the quantized images are more amenable to the type of processing used for grayscale ones. Methods for processing grayscale images cannot be simply extended to color images because they rely on the fact that each gray-level is described by a single number and the fact that a relation of full order can be easily established on the set of those numbers. Color spaces (such as RGB or Lab) are, on the other hand, 3-D. The proposed color quantization, i.e., color space sampling and numbering of sampled points, makes methods for processing grayscale images extendible to color images. We illustrate possible processing of color images by first introducing the basic average and difference operations and then implementing edge detection and compression of color quantized images.
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