Image Retrieval Using Dominant Color Descriptor.
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Content Based Image Retrieval (CBIR) is a technique of finding appropriate images based on the features that are automatically extracted from the image itself. An important low-level feature in any image is dominant color. Dominant Color Descriptor (DCD) was proposed by MPEG-7 and is extensively used in image retrieval. An improvement over DCD was Linear Block Algorithm (LBA). In this paper, we propose an improved similarity measure for dominant color descriptor. We improve LBA by making two significant changes. First is improvement in the similarity measure and second is local implementation through region based dominant colors. The proposed similarity measure takes into account the number of dominant colors of the two images to be compared. The earlier well known methods like MPEG-7 DCD and LBA use the RGB color components and their percentages to find similarity between the query and target images. In our work, it is now weighted by the number of dominant colors in the two images and their mutual distances. The experimental results demonstrate that the proposed method outperforms LBA and other prominent color based retrieval techniques.
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RGB color model
Similarity (geometry)
Feature (linguistics)
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In content-based image retrieval,both color and texture are always frequently used and also important features.But only a method applies to image retrieval could not express attributes of image completely.A new method for image retrieval using texture edge histogram,dominant color and color layout was proposed.Retrieval experiments using combined texture and color feature were carried out according to the evaluation criterion used in MPEG-7.Experiments show that the retrieval results obtain from combined-features fits more closely with human perception,and the proposed method yields better retrieval performance.
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This paper presents results on the study of MPEG-7 visual descriptors for deformable object retrieval. A database of 819 handbag images with shape masks are constructed with different variations such as morphing, illumination changes, view point changes and color changes. For color descriptors, all of 4 MPEG-7 color descriptors of Dominant Color descriptor, Color Structure descriptor, Color Layout descriptor and Scalable Color descriptor are tested. For texture descriptor, Homogeneous Texture descriptor and Edge Histogram descriptor are tested. For shape descriptors, Contour-based and Region based descriptor are tested. The retrieval rate of the descriptors and correlation of each pair of descriptors are studied. The result shows that the Scalable Color descriptor is the best in terms of retrieval rate and the color descriptors are relatively highly correlated among themselves.
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This paper presents an efficient image feature representation method, namely angle structure descriptor (ASD), which is built based on the angle structures of images. According to the diversity in directions, angle structures are defined in local blocks. Combining color information in HSV color space, we use angle structures to detect images. The internal correlations between neighboring pixels in angle structures are explored to form a feature vector. With angle structures as bridges, ASD extracts image features by integrating multiple information as a whole, such as color, texture, shape and spatial layout information. In addition, the proposed algorithm is efficient for image retrieval without any clustering implementation or model training. Experimental results demonstrate that ASD outperforms the other related algorithms.
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A new spatial dominant color descriptor(SDCD) is proposed firstly,based on the analysis of the dominant color descriptor(DCD) and the color structure descriptor(CSD) recommended in MPEG-7 standard.It uses some representative colors and the spatial distribution information among them to represent the whole image.Experimental results show that SDCD combines the advantages of DCD and CSD such as exactness,efficiency and compactness.
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Image mosaic or image montage is an image made up of many other images. In this paper, we propose a methodology of generating image mosaics. Our image mosaic generating system divides an input image into many tiles; and then for each tile, it fetches the image with the most similar content from an image database and replaces the tile with the image. CBIR techniques and the approach which we propose are used in unison to select the image with the most similar content to a tile. Due to the large size of our image collection, a compact visual feature representation, as well as its distance metric, has been used to reduce the storage and processing time. There is a set of parameters used in our system such as the number of tiles and the size of the image database/tiles which controls the procedure of image mosaic generating.
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sed
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This chapter studies the design of color descriptors. A number of different linear and nonlinear color spaces are presented and studied. The chapter examines the problem of color space quantization, which is necessary for producing color descriptors such as color histograms. The author describes the extraction of color histograms from images and metrics for matching. Image-retrieval experiments are also discussed.
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Dominant color is a compact and efficient descriptor which employs representative colors to characterize the color information in the interesting region of an image. Dominant color descriptor is suitable for representing local features of images and can be used for quick retrieval in large image databases. A method for extracting image dominant color based on region growing algorithm was proposed in this paper. The steps of proposed method are as follows: Firstly, the color space of an image is transformed from RGB to HSV; Secondly, region growing algorithm is used to divide an image into color regions, and regions of which the areas are less than 1 per cent of the image will be filtered out; Thirdly, neighbor regions with similar color are then merged, and if area of the merged region compared with the image is more than 5%, its color will be considered as a dominant color. Proposed method was compared with well known color histogram based algorithm in this paper. Two color evaluation criterion proposed by MPEG-7 standards, i.e. ANMRR (average normalized modified retrieval rank) and ARR (average retrieval rate) were used in experiments respectively. According to MPEG-7 standards, less ANMRR or larger ARR indicate a good image retrieval result. For a 468 images database with 4 differences similar image groups, ANMRR and ARR of proposed method reduced 10.7% and increased 7.46% respective compared with color histogram based algorithm. The experiment results showed, according to ANMRR and ARR, the performance of proposed method is better than that of compared algorithm.
Color histogram
RGB color model
HSL and HSV
Color balance
Color normalization
Color quantization
Color depth
High color
RGB color space
False color
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The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.
Content-Based Image Retrieval
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