Color Quantization Scheme Research Articles

칼라 영상 객체 분할을 이용한 게임 콘텐츠 분류 서비스 방안에 관한 연구

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

Content-based Image Retrieval using Image Partitioning with Color Histogram and Wavelet-based Color Histogram of the Image

paper presents two content-based image retrieval algorithms that are based on image partitioning. The retrieval in the first algorithm is based only on the image color feature represented by the color histogram, while the retrieval in the second one is based on the image color and texture features represented by the color histogram and Haar wavelet transform, respectively. In these algorithms, each image in the database and the query image are divided into 4-equal sized blocks. Color and texture features are extracted for each block. Distances between the blocks of the query image and the blocks of a database image are calculated, then, the similarity between the query image and the database image is calculated by finding the minimum cost matching based on most similar highest priority (MSHP) principle. A CBIR system that implements the proposed algorithms has been developed. To evaluate the effectiveness of the proposed algorithms, experiments have been carried out using different color quantization schemes for three different color spaces (HSV, YIQ and YCbCr) with two similarity measures, namely the Histogram Euclidean Distance and Histogram Intersection Distance. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments.

A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

spaces, color histograms, histogram distance measurements, size and quantization play an important role in retrieving images based on similarities. This paper presents a study of the effect of color quantization schemes for different color spaces (HSV, YIQ and YCbCr) on the performance of content-based image retrieval (CBIR), using different histogram distance measurements (Histogram Euclidean Distance and Histogram Intersection Distance). For the purpose of this study, a CBIR system that implements two content-based image retrieval algorithms has been developed. The first algorithm is based only on the color feature, while the second one is based on combination of the color and texture features. The color histogram is used for image color feature extraction and Haar wavelet transform is used for image texture feature extraction. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments of this study. The experimental results show which histogram distance measurement is best, which color space gives better retrieval precision, and the best quantization schemes for the considered color spaces, when using only the color feature, and when using a combination of the color and texture features.

The Study of the Furniture Design Based on Product Morphological Semantics

morphological semantics is an important theory of kansei engineering. Making an analysis of psychological feeling of modern furniture by using molding semantics, pointed out that the awareness of modern furniture in material, shape, structure, color quantization scheme, which make the design of furniture products more in line with the need for user’s cognition, and provide effectiveness for furniture products’ design as well as manufacturing, make furniture to meet people to use the functional requirements and satisfy people’s aesthetic desire.

Color image segmentation using adaptive color quantization and multiresolution texture characterization

This paper presents a new hybrid color image segmentation approach, which attempts two different transforms for texture representation and extraction. The 2-D discrete wavelet transform that can express the variance in frequency and direction of textures, and the contourlet transform that represents boundaries even more accurately are applied in our algorithm. The whole segmentation algorithm contains three stages. First, an adaptive color quantization scheme is utilized to obtain a coarse image representation. Then, the tiny regions are combined based on color information. Third, the proposed energy transform function is used as a criterion for image segmentation. The motivation of the proposed method is to obtain the complete and significant objects in the image. Ultimately, according to our experiments on the Berkeley segmentation database, our techniques have more reasonable and robust results than other two widely adopted image segmentation algorithms, and our method with contourlet transform has better performance than wavelet transform.

Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.

Modeling human color categorization

A unique color space segmentation method is introduced. It is founded on features of human cognition, where 11 color categories are used in processing color. In two experiments, human subjects were asked to categorize color stimuli into these 11 color categories, which resulted in markers for a Color LookUp Table (CLUT). These CLUT markers are projected on two 2D projections of the HSI color space. By applying the newly developed Fast Exact Euclidean Distance (FEED) transform on the projections, a complete and efficient segmentation of color space is achieved. With that, a human-based color space segmentation is generated, which is invariant for intensity changes. Moreover, the efficiency of the procedure facilitates the generation of adaptable, application-centered, color quantization schemes. It is shown to work excellently for color analysis, texture analysis, and for Color-Based Image Retrieval purposes.

Fast Trellis-Coded Color Quantization of Images

We examine color quantization of images using trellis-coded quantization (TCQ) in both the RGB domain and YUV domain. Together with a simple dithering scheme, an eight-bit trellis-coded color quantizer reproduces images that are visually indistinguishable from the 24-bit originals. It can be viewed as a predictive trellis-coded color quantization scheme. We also study trellis-coded vector quantization (TCVQ) for color quantization. Our proposed TCQ-based color quantization schemes are universal in the sense that no training or look-up table is needed. The complexity of TCQ is linear with respect to image size, making trellis-coded color quantization suitable for color printing, real-time interactive graphics and window-based display environment.