Image Saturation And Reflection Is The Process Of Image Retrieval

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CHAPTER III COLOR DESCRIPTION AND EXTRACTION 3.1 INTRODUCTION Image retrieval is the process of handling large volume of image database in order to achieve the efficiency in identifying similar images over the retrieved results. In Image retrieval, a choice of various techniques is used to represent images for searching, indexing and retrieval with either supervised or unsupervised learning models. The color feature extraction process consists of two parts: grid based representative of color selection [B.S.Manjunath, 2001] and discrete cosine transform with quantization. Color feature extraction is a very compact and resolution invariant representation of high speed image retrieval systems and it has been designed to efficiently represent the …show more content…

HSx color space contains the HSI, HSV, HSB color spaces, that is most similar to human color perception in which HS stands for Hue and Saturation. I, V, and B stand for Intensity, Value, and Brightness, respectively. In this regard, Hue describes the actual wavelength of the color, Saturation is the measure of the purity of the color (For example, red is 100% saturated color, but pink is not 100% saturated color because it contains an amount of white) and the Intensity describes the lightness of the color [Tsang P.W.M., Tsang W.H., …show more content…

In particular, given a set of n vectors, k-means clustering groups them into k clusters (i.e., subsets) in such a way that each vector belongs to the cluster with the closest mean [Suman Tatiraju and Avi Mehta, 1997]. The problem is computationally NP-hard, and suboptimal greedy algorithms have been developed for k-means clustering. In feature learning, k-means clustering can be used to group an unlabeled set of inputs into k clusters, and then use the centroids of these clusters to produce features. These features can be produced in several ways. The simplest way is to add k binary features to each sample, where each feature j has value one ith and jth centroid learned by k-means is the closest to the sample under

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