12-03-2012, 09:52 PM
i want ppts abut dis topic plz provide me
12-03-2012, 09:52 PM
i want ppts abut dis topic plz provide me
15-06-2012, 02:12 PM
Computational Perceptual Features for Texture
Representation and Retrieval Computational Perceptual Features for Texture.pdf (Size: 1.76 MB / Downloads: 73) Abstract A perception-based approach to content-based image representation and retrieval is proposed in this paper.We consider textured images and propose to model their textural content by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, and busyness. INTRODUCTION TEXTURE has been extensively studied and used in literature since it plays a very important role in human visual perception. Although there exists no precise and universal definition of texture, some intuitive concepts can be defined about texture. Texture refers to the spatial distribution of grey-levels and can be defined as the deterministic or random repetition of one or several primitives in an image. Microtextures refer to textures with small primitives while macrotextures refer to textures with large primitives [31]–[33]. Texture analysis techniques have been used in several domains such as classification, segmentation, shape from texture and image retrieval. RELATED WORKS There are some works published in literature on the subject of human visual perception since the early studies done by Julesz [21] and Bergen et al. [10]. However, there are two main works that are closely related to our work. The first work is done by Tamura et al. [30] and the second work is done by Amadasun et al. [8]. Each of the two has proposed computational measures for a set of textural features. The work of Tamura et al. [30] was based on the co-occurrence matrix and the work of Amadasun et al. [8] was based on a variant of the co-occurrence matrix called NGTDM (neighborhood grey-tone difference matrix). PERCEPTUAL TEXTURAL FEATURES We can find a long list of perceptual textural features in literature. However, only a small list of features is considered as the most important. This list comprises coarseness, contrast and directionality. Other features of less importance are busyness, complexity, roughness and line-likeness [8], [30]. In this study, we have considered four perceptual features, namely coarseness, directionality, contrast and busyness. In the following, we give conceptual definitions of each of these features. MULTIPLE REPRESENTATIONS: AUTOCORRELATION FUNCTION versus ORIGINAL IMAGES The set of computational measures simulating perceptual textural features that we will define in the next section can be based on two representations (or viewpoints): original images or the autocorrelation function associated with images. Applying computational measures on one or the other of the two representations does not hold the same results. We will show at the end of this paper that, in the framework of content-based image retrieval, adopting multiple representations will allow significant improvement in retrieval effectiveness. CONCLUSION A new perceptual model based on a set of computational measures corresponding to perceptual textural features, namely coarseness, directionality, contrast, and busyness, was introduced in this paper. Computational measures are based on two different representations (viewpoints): original images and the autocorrelation function associated with images. Coarseness was estimated as an average of the number of extrema. Contrast was estimated as a combination of the average amplitude of the gradient, the percentage of pixels having the amplitude superior to a certain threshold and coarseness itself. Directionality was estimated as the average number of pixels having the dominant orientation(s).
27-07-2012, 01:36 PM
i want to know the algorithm used in the computational perceptual features for texture representation and retrieval |
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