Content Based Images Retrieval Based on PDE Model: use of Curvelet Transform and Gabor Wavelets

Abstract In this paper, we present a new method for content based images retrieval. It relies on the use of an image decomposition model based on PDE to split an image into two components : a geometrical component and a texture(oscillatory) component. We propose to characterize image without extracting local features, by using global information extracted from the image decomposition model on PDE. The effective feature extraction is performed not on the original image but on its components. Image feature based on curvelet transform and Gabor wavelet, are then calculated. Our system was tested on Brodatz database. Retrieval efficiency and accuracy combining image decomposition model, and both curvelet method and Gabor wavelet based features are found to be superior to other existing methods. Keywords : Content Based Image Retrieval, Curvelet Transform, Gabor Wavelets, Image Decomposition. 1. Introduction Content Based Image Retrieval (CBIR) has become one of the most active research areas. It has been an active area of research due to its applications in various fields like commerce, government, academia, and hospitals. Therefore; many image retrieval systems such as QBIC, MARS, Virage, FIDS etc. have been built. The CBIR systems can be classified broadly into two categories as Low level feature based system and High level/Semantic feature based system. Low level features are general features and computed from pixel values. But, high level features are abstract attributes involving a significant amount of reasoning. Feature extraction is one of the most important tasks for efficient and accurate image retrieval purpose. Shape, texture and color are three main groups of features that are being used in CBIR systems. Shape is represented by circularity, eccentricity, Fourier descriptor [1], moment invariants [2], histogram of values after applying Sobel edge filter [3], edge layout vector [4], shape context descriptor [5] etc. A variety of techniques have been used for measuring texture such as co-occurrence matrix [6], Gabor filter [7] and fractals [8]. Texture is an important feature of natural images. A successful CBIR system must be able to deal with textured images in practical application. Several models have been proposed to analyze textures, such as grey-level co-occurrence matrices, Markov random field model [9], and simultaneous auto-regressive model [10], and World decomposition model [11]. The most of spatial domain texture analysis models have a fundamental weakness that the image is analyzed at a single scale. This aspect can be improved with a joint spatial frequency multi-channel representation methodology. Usually a joint spatial frequency multi-channel Representation methodology is used to deal with scale changes involved in textured image. However, this leads often to a relative efficiency according to the complexity and wealth information which contain the considered image database. Alternative methods of texture analysis for image retrieval include the use of Gabor filters [7], Wavelet [12] and DCT [13]. However, most of them are not able to accurately capture the edge information which is the most important texture feature in an image. Wavelet transform fails to represent objects containing randomly oriented edges and curves as it is not good at representing line singularities. Gabor filters are found to perform better than wavelet transform in representing textures and retrieving images due to its multiple orientation approach [7]. However, due to the loss of spectral information in Gabor filters they cannot effectively represent images. This affects the CBIR performance. Consequently, a more robust mechanism is necessary to improve CBIR performance. To achieve a complete coverage of the spectral domain and to capture more orientation information, Curvelet transform has been developed. It captures edge information or texture information more accurately than Wavelet and Gabor filters [14, 15].