Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> In image retrieval based on color, the weighted distance between color histograms of two images, represented as a quadratic form, may be defined as a match measure. However, this distance measure is computationally expensive and it operates on high dimensional features (O(N)). We propose the use of low-dimensional, simple to compute distance measures between the color distributions, and show that these are lower bounds on the histogram distance measure. Results on color histogram matching in large image databases show that prefiltering with the simpler distance measures leads to significantly less time complexity because the quadratic histogram distance is now computed on a smaller set of images. The low-dimensional distance measure can also be used for indexing into the database. > <s> BIB001 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval. <s> BIB002 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> This paper presents an overview of color and texture descriptors that have been approved for the Final Committee Draft of the MPEG-7 standard. The color and texture descriptors that are described in this paper have undergone extensive evaluation and development during the past two years. Evaluation criteria include effectiveness of the descriptors in similarity retrieval, as well as extraction, storage, and representation complexities. The color descriptors in the standard include a histogram descriptor that is coded using the Haar transform, a color structure histogram, a dominant color descriptor, and a color layout descriptor. The three texture descriptors include one that characterizes homogeneous texture regions and another that represents the local edge distribution. A compact descriptor that facilitates texture browsing is also defined. Each of the descriptors is explained in detail by their semantics, extraction and usage. The effectiveness is documented by experimental results. <s> BIB003 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> We have analyzed the properties of the HSV (hue, saturation and value) color space with emphasis on the visual perception of the variation in hue, saturation and intensity values of an image pixel. We extract pixel features by either choosing the hue or the intensity as the dominant property based on the saturation value of a pixel. The feature extraction method has been applied for both image segmentation as well as histogram generation applications - two distinct approaches to content based image retrieval (CBIR). Segmentation using this method shows better identification of objects in an image. The histogram retains a uniform color transition that enables us to do a window-based smoothing during retrieval. The results have been compared with those generated using the RGB color space. <s> BIB004 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases. <s> BIB005 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> A new dominant color structure descriptor (DCSD) is proposed in this paper. It is designed to provide an efficient way to represent both color and spatial structure information with single compact descriptor. The descriptor combines the compactness of dominant color descriptor (DCD) and the retrieval accuracy of color structure descriptor (CSD) to enhance the retrieval performance in a highly efficient manner. The feature extraction and similarity measure of the descriptor are designed to address the problems of the existing descriptors while utilize the advantages of them. Experimental results show that DCSD has a significant improvement on both retrieval performance and descriptor size over DCD. An eight-color DCSD (DCSD 8) gives an averaged normalized modified retrieval rate (ANMRR) of 0.0993 using MPEG-7 common color dataset, outperforming compact configurations of scalable color descriptor and color structure descriptor with smaller descriptor size. <s> BIB006 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> Color has been extensively used in the process of image retrieval. The dominant color descriptor (DCD) that was proposed by MPEG-7 is a famous case in point. It is based on compactly describing the prominent colors of an image or a region. However, this technique suffers from some shortcomings; especially with respect to object-based image retrieval. In this paper, a new semantic feature extracted from dominant colors (weight for each DC) is proposed. The newly proposed technique helps reduce the effect of image background on image matching decision where an object's colors receive much more focus. In addition, a modification to DC-based similarity measure is also proposed. Experimental results demonstrate that the proposed descriptor with the similarity measure modification performs better than the existing descriptor in content-based image retrieval application. The proposed descriptor considers as step forward to the object-based image retrieval. <s> BIB007 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Color Features <s> 2 Abstract: Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods. <s> BIB008
The human visual system is very sensitive to colors and possesses the capability to differentiate between thousands of colors. Color sensation is a prime feature of our visual perception system that guides us towards object recognition. Hence it has been widely exploited for representing image content. Colors in digital images may be represented in a variety of color models. These color models are 3-dimensional coordinate systems for representing them as points in that space. Numerous models exist including RGB, HSV, YCbCr and CIELab etc BIB008 . All of these color representations have been utilized for color based content description. The simplest way to represent colors in an image is to populate color histograms in which a count of the number of pixels of various colors is accumulated BIB004 BIB005 BIB001 . Color quantization is generally employed to reduce the number of colors in the image into a few representative colors. This helps making the description process convenient and efficient by reducing memory requirements. The problem with such histograms is that they carry absolutely no information regarding the spatial arrangement of those colors in the image. Hence methods were introduced in an attempt to incorporate some spatial layout information. Color correlograms (CCG) BIB002 encode color layout as probabilities of occurrence of pairs of colors at a fixed distance in the image. Hence CCG performs better than color histograms. Several color descriptors have also been introduced in MPEG-7 standard. In Dominant Color Descriptor (DCD) BIB003 , colors in the image are quantized into a number of dominant colors using clustering methods. It then encodes dominant colors into triads of color values, percentages of colors and their variances. The drawback associated with this scheme is the inability of represent spatial relationship of colors. The Color Structure Descriptor (CSD) BIB003 populates a histogram by taking local distributions of colors in the image. A small window is moved from pixel to pixel in the entire image. The histogram bins associated with colors located in the small window are incremented. In this way spatial layout of colors in the image is captured. The Scalable Color Descriptor (SCD) BIB003 quantizes the color image into 256 colors in the HSV color space. The hue component (H) is quantized into 16 levels. Whereas the remaining two components saturation (S) and value (V) are quantized into 4 components each. A novel Haar transform encoding is then used which facilitates scalable representation of the description. Also provides complexity scalability to the feature extraction and matching procedures. The Color Layout Descriptor (CLD) BIB003 represents the spatial arrangement of the dominant colors in the region of interest on a regular grid. It is a compact and efficient descriptor for browsing and searching. It is suitable for both still images and videos. The MPEG-7 color descriptors were further improved by researchers during the last decade. Weaknesses were discovered in their representations and improvements were brought into them. Dominant Color Structure Descriptor (DCSD) BIB006 combines the characteristics of DCD and CSD into a single descriptor. The image is first quantized into dominant colors. Then the local spatial color distribution is captured by moving a small window over the image and incrementing the corresponding color bins. Unlike DCD, the DCSD represents color structure bins instead of dominant colors in the triads. In another scheme called the Weighted Dominant Color Descriptor (WDCD) BIB007 adds weights to dominant colors by taking into consideration whether the color contributes to the salient object(s) in the image or the background. These weights represents the importance of a dominant color in the image. A high weight is assigned to the important colors in the image and contributes the most while comparing images. The colors having low weights are assumed to belong to the background and hence contributes the least during the image matching phase. This improved retrieval accuracy of the CBIR system significantly. The WDCD descriptor was tested on natural images as well as cartoon images.