Data Mining - Concepts and Techniques.

International Journal of Computer and Applications

Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-23/issue-2/029901/Color-Appearance-Models/10.1117/1.JEI.23.2.029901.pdf

Color Appearance Models

Biometric Recognition - Security and Privacy Concerns.

There is an abundancy of systems characterized by parabolic PDEs in science and engineering, especially in chemistry and physics. These systems have a scalar variable, we generally call time, defining the evolution of the system under consideration. The governing equation(s) involves the unknown(s) and their first order partial derivative(s) with respect to this variable. Time variant Schrodinger equations where the unknown is the wavefunction which is responsible for the probability density for the system and Liouville equations for the statistical mechanics where the unknown is somehow responsible for a density in the systems’ phase space (here we use the plurality since both case may differ from Hamiltonian to Hamiltonian). Certain PDE(s), depending on so-called spatial coordinates, govern the behavior of the system in these and similar cases even though the partial differential equation nature is not necessarily needed. Hence we give the following equation for more

MATHEMATICAL MODELS and METHODS in APPLIED SCIENCES

695 Abstract—In this paper a novel method for image retrieval based on texture feature extraction using Vector Quantization (VQ) is proposed. We have used Linde-Buzo-Gray (LBG) and Kekre’s Proportionate Error (KPE) algorithms for texture feature extraction. The image is first divided into pixel blocks of size 2X2, each pixel with red, green and blue component. A training vector of dimensions 12 is created using this block. Collection of such training vectors is a training set. To generate the texture feature vector (size of codebook 16X12) of the image, popular LBG and KPE algorithms are applied on the initial training set. Results are compared with the Gray Level Co-occurance Matrix (GLCM) method. The proposed method requires 89.10% less computations compared to the GLCM method. The LBG and KPE based image retrieval techniques give higher precision and recall values than GLCM based method, which concludes that the proposed techniques give better texture feature discrimination capability than GLCM. 1

Image Retrieval using Texture Features extracted from GLCM, LBG and KPE

With the tremendous growth of ICT (Information and Communication Technology), we are able to generate, store, share and transfer enormous amount of information. World Wide Web have further made is easy to access the information anytime, anywhere in the world. With the advent of high capacity communication links and storage devices even most of the information generated is of multimedia in nature. Images have major share in this information and the number of image achieves are growing with the jet speed Just having the tremendous amount of information is not useful unless we do not have the methodologies to effectively search the related data from it in minimum possible duration. The relativity of the image data is application specific. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval (CBIR) system. CBIR extracts the features of query image and try to match them with the extracted features of images in the database. Then based on the similarity measures and threshold the best possible candidate matches are given as result. There have been many approaches to decide and extract the features of images in the database. Binary truncation Coding based features is one of the CBIR methods proposed using color features of image. The approach basically considers red, green and blue planes of image together to compute feature vector. Here we have augmented this BTC based CBIR as BTC-RGB and Spatial BTC-RGB. In BTC-RGB feature vector is computed by considering red, green and blue planes of the image independently. While in Spatial BTC-RGB, the feature vector is composed of four parts. Each part is representing the features extracted from one of the four non overlapping quadrants of the image. The new proposed methods are tested on the 1000 images database and the results show that the precession is improved in BTC-RGB and is even better in Spatial BTC-RGB.

Image retrieval using augmented block truncation coding techniques

novel technique for image retrieval using the color- texture features extracted from images based on vector quantization with Kekre's fast codebook generation is proposed. This gives better discrimination capability for Content Based Image Retrieval (CBIR). Here the database image is divided into 2x2 pixel windows to obtain 12 color descriptors per window (Red, Green and Blue per pixel) to form a vector. Collection of all such vectors is a training set. Then the Kekre's Fast Codebook Generation (KFCG) is applied on this set to get 16 codevectors. The Discrete Cosine Transform (DCT) is applied on these codevectors by converting them to column vector. This transform vector is used as the image signature (feature vector) for image retrieval. The method takes lesser computations as compared to conventional DCT applied on complete image. The method gives the color-texture features of the image database at reduced feature set size. Proposed method avoids resizing of images which is required for any transform based feature extraction method.

Image retrieval using color-texture features from dct on vq code vectors obtained by kekres fast codebook generation

Here we are presenting some novel techniques for squirting colors in grayscale images. The problem of coloring grayscale images has no exact solution. Here we are attempting to minimize the human efforts needed in manually coloring the grayscale images. We need human interaction only to find a reference color image, then the job of transferring color traits from reference color image to grayscale image is done by proposed techniques. In these techniques, the color palette is prepared using pixel windows of some degrees taken from reference color image. Then the grayscale image is divided into pixel windows with same degrees. For every window of grayscale image the palette is searched for equivalent color values, which could be used to color grayscale window. In the whole process the luminance values of reference color image and target grayscale image are only matched and based on best possible match the respective chromaticity values of color image are transferred to grayscale image. For palette preparation first we used RGB color space and then Kekre's LUV color space[9]. Results with Kekre's LUV color space were comparatively better. To improve the searching time through color palette the exhaustive and Kekre's fast search are used.

Color Traits Transfer to Grayscale Images

The reversible method of converting color images into gray images has been recently proposed using YCbCr color space and Discrete Wavelet Transform (DWT). The method embeds the colors of original colored image into the gray image using DWT. The gray image is being converted to textured gray because of color information embedding. This textured gray image could be either printed or sent to the receiver with the size less than the original color image. After receiving the textured gray image, receiver applies DWT on it and extracts the veiled color information to reconstruct the color image. The printed textured gray image is scanned for digitization and then the techniques discussed here can extract the colors of image to recreate the original color image. Using Kekre's LUV color space for the technique improves the quality of recreated colored image. Also the matted gray image has better quality and eye perception than using YCbCr color space. Applications of this can be compressing the image data for transmission and printing the color images using black color cartridge only and regenerating the color images afterwards. The improvement in recovered color image and matted gray image are shown in results and supported by statistical data.

Sudeep D. Thepade

Papers

Image Retrieval using Texture Features extracted from GLCM, LBG and KPE

Image retrieval using augmented block truncation coding techniques

Image retrieval using color-texture features from dct on vq code vectors obtained by kekres fast codebook generation

Color Traits Transfer to Grayscale Images

Improving `Color to Gray and Back' using Kekre's LUV Color Space