Abstract-Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comp~rehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated. Index Terms-Digital libraries, image database, content-based image retrieval, texture analysis, Gabor wavelets. RETRIEVAL of image data based on pictorial queries is an interesting and challenging problem. The recent emergence of multimedia databases and digital libraries m,lkes this problem all the more important. While manual image annotations can be used to a certain extent to help image search,, the feasibility of such an approach to large databases is a qu'estionable issue. In some cases, such as face or texture patterns, simple textual descriptions can be ambiguous and often inadequate for database search. The objective of this paper is to study the use of texture as an image feature for pattern retrieval. An image can be considered as a mosaic of different texture regions, and the image features associated with these regions can be used for search and retrieval. A typical query could be a region of interest provided by the user, such as outlining a vegetation patch in a satellite image. The input information in such cases is an intensity pattern or texture within a rectangular window. See Fig. 6 for an example of a texture based browsing application. Texture analysis has a long hi~~tory and texture analysis algorithms range from using random field models to multiresolution filtering techniques such as the wavelet transform. Several researchers have considered the use of such texture features for pattern retrieval [181, [191. This paper focuses on a multiresolution representation based on Gabor filters. The use of Gabor filters in extracting textured image features is motivated by various factors. The Gabor representation has been shown to be optimal in the sense of minimizing the joint two-dimensional uncertainty in space and frequency [4]. These filters can be considered as orientation and scale tunable edge and line (bar) detectors, and the statistics of these microfeatures in a given region are often used to characterize the underlying texture information. Gabor features have been used in several image .analysis applications including texture classification and segmentation [I], [141, image recognition [51, [81, [131, image registration, and motion tracking [151. The main contributions of this paper are summarized below: 1) A simple texture feature representation based on Gabor features is proposed, and a filter design strategy is sug- gested to reduce the redundancy in the representation. 2) An adaptive filter selection algorithm is proposed which can facilitate fast image browsing. 3) A detailed comparison with the performance of three other multiscale texture features is provided. Our proposed representation compares favorably in terms of feature computations and retrieval accuracy. 4) Finally, an application to browsing large air photos is demonstrated. A two dimensional Gabor function g(x, y) and its Fourier transform G(u, v) can be written as: where o,, = 1/2zo, and o, = 1/2zoy. Gabor functions form a complete but nonorthogonal basis set. Expanding a signal using this basis provides a localized frequency description. A class of self-similar functions, referred to as Gabov wavelets in the following discussion, is now considered. Let g(x, y) be the mother Gabor wavelet, then this self-similar filter dictionary can be obtained by appropriate dilations and rotations of g(x, y) through the generating function: g,n,(x, y) = a-"~(x', y'), a > 1, m, n = integer x' = a-"(xcose + sine), and y' = a-m(-xsin~ + y cose), where 0 = nz/K and K is the total number of orientations. The scale factor a-" in (3) is meant to ensure that the energy is independent of rn. The nonorthogonality of the Gabor wavelets implies that there is redundant information in the filtered images, and the following strategy is used to reduce this redundancy. Let U, and U , denote the lower and upper center frequencies of interest. Let K be the number of orientations and S be the number of scales in the multiresolution decomposition. Then the design strategy is to ensure that the half-peak magnitude support of the filter responses in the frequency spectrum touch each other as shown in Fig. 1 . This results in the following formulas for computing the filter parameters o, and o, (and thus ox and o,). where W = U, and m = 0, 1, ..., S -1. In order to eliminate sensitivity of the filter response to absolute intensity values, the real (even) components of the 2D Gabor filters are biased by adding a constant to make them zero mean (This can also be done by setting G(0,O) in (2) to zero).
This directional limitation was removed by using Gabor wavelet feature-based texture analysis in REF .
Texture features for browsing and retrieval of image data
IEEE Trans. Pattern Anal. Mach. Intell.
IEEE Trans. Pattern Anal. Mach. Intell.
Computer Science