Comparison of Content Based Image Retrieval Systems Using Wavelet and Curvelet Transform

Abstract

The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and efficiently. This paper implements a CBIR system using different feature of images through four different methods, two were based on analysis of color feature and other two were based on analysis of combined color and texture feature using wavelet coefficients of an image. To extract color feature from an image, one of the standard ways i.e. color histogram was used in YCbCr color space and HSV color space. Daubechies' wavelet transformation and Symtel's wavelet transform were performed to extract the texture feature of an image. In this paper a color image retrieval system is illustrated, in which the novelty lies in the use of a fuzzy partition of the HSV color space and wavelet transformation of the fuzzified new image. To increase efficiency of the system finally an image retrieval method was proposed using curvelet transform of an image, which provides an opportunity to extract more accurate texture feature for image retrieval. After obtaining all experimental results, a comparative study was done. From the result it was inferred that curvelet based method gave a better performance as compared to other methods.

Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>

“…This is confirmed by the existence of a number of articles [26] [27] [28] [29] [30]. At the same time the HSV color system is preferable in implementing the contrast change procedure.…”

A review and analysis of content based image retrieval system

“…In 2012, Das et al [25] suggested a CBIR system that can apply on different feature of images. To focus shading highlights from a photo, one of the standard ways i.e.…”

Result Analysis and Comparison of Hybrid Method based on Local Binary Pattern (LBP) and Color Moment (CM) for Efficient Image Retrieval

“…With expansive information sets, there is probability of high dimensionality. In 2012, Das et al [26] implemented a CBIR system which uses different feature of images by applying four methods, two methods were based on the color feature analysis and rest two were based on color and texture feature analysis using wavelet coefficients of an image. In 2007, Jain et al [27] proposed an algorithm for retrieving images.…”