SASI: A new texture descriptor for content based image retrieval

2001-10-10
Carkacioglu, A
Yarman-Vural, F
In this paper, a new texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation functions calculated over a set of directional moving windows. SASI defines a set of windows to extract and measure various structural properties of texture by using a spatial multiresolution method. Although it works in spatial domain, it measures the spectral information of a given texture. Experimental results, performed on digitized Brodatz Album, indicate that SASI is very successful in identifying the "similar" textures.

Suggestions

SASI: a generic texture descriptor for image retrieval
Carkacioglu, A; Yarman-Vural, F (Elsevier BV, 2003-11-01)
In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Ga...
Bayesian classification of image structures
Goswami, D.; Kalkan, Sinan; Krüger, N. (2009-11-09)
In this paper, we describe work on Bayesian classifiers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classifiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensionality. The built classifier is tested on standard and non-standard images. © 2009 Springer Berlin Heidelberg.
Shape : representation, description, similarity and recognition
Arıca, Nafiz; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2003)
In this thesis, we study the shape analysis problem and propose new methods for shape description, similarity and recognition. Firstly, we introduce a new shape descriptor in a two-step method. In the first step, the 2-D shape information is mapped into a set of 1-D functions. The mapping is based on the beams, which are originated from a boundary point, connecting that point with the rest of the points on the boundary. At each point, the angle between a pair of beams is taken as a random variable to define...
Parallel linear solution of large structures on heterogeneous PC clusters
Kurç, Özgür (2006-12-01)
In this paper, a parallel solution framework for the linear static analysis of large structures on heterogeneous PC clusters is presented. The framework consists of two main steps; data preparation and parallel solution. The parallel solution is performed by a substructure based method with direct solvers. The aim of the data preparation step is to create the best possible substructures so that the condensation times of substructures are balanced. Examples which illustrate the applicability and the efficien...
Change detection in aerial images
Borchani, M; Cloppet, F; Atalay, Mehmet Volkan; Stamon, G (2004-01-01)
This paper deals with how to characterize texture and how to get a good description of images with a minimal number of parameters. This procedure is more objective than textual data. Texture characterization has been used in a matching system to detect changes in couples of aerial images taken at two different times using different order of statistics to describe images. The results are quite encouraging.
Citation Formats
A. Carkacioglu and F. Yarman-Vural, “SASI: A new texture descriptor for content based image retrieval,” 2001, p. 137, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65536.