TexRet: A Texture Retrieval System Using Soft-Computing

TexRet: A Texture Retrieval System Using Soft-Computing

Girish Katkar (ACS College, Koradi, India) and Pravin Ghosekar (Dhanwate National College, India)
Copyright: © 2012 |Pages: 10
DOI: 10.4018/jissc.2012010104
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The TEXRET-System, a texture retrieval system based on soft-computing technologies is being developed. The importance of this kind of system is increasing due to the massive access to digital image databases, which also demand the existence of systems that can understand human high-level requests. The TEXRET system has the following features: (i) direct access from the Internet, (ii) high interactivity, (iii) texture retrieval using human-like or fuzzy description of the textures, (iv) content-based texture retrieval using user-feedback, and (v) synthesis or generation of the requested textures when these are not found in the database, which allows a growing of the database. One of the main system features is synthesis of the requested textures when these are not found in the database, which allows a growing of the database. Missing textures are synthesized interactively using Markov Random Fields and interactive genetic algorithms. This paper is centered on the texture synthesis of the textures.
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2. The Texret-System

The TEXRET-System, whose block diagram is shown in Figure 1, is made of the FI (Fuzzy Interface), the Q2TPT (Qualitative to Quantitative Textural Properties Transformation), the TR (Texture Retrieval), the TG (Texture Generation), and the EPA (Evolutionary Parameter Adjustment) modules. The on-line phase of the texture retrieval process works as follows: A human user makes a query of a texture using a subjective, linguistic or human-like texture description. The FI module enters this description into the system using a fuzzy representation of it. The Q2TPT module interprets the query and translates it into a quantitative texture description that is implemented using Tamura Descriptors. This qualitative description is used by the TR module to search the texture in the database. In the case that the texture is not found in the database, the user can choose the automatic generation of it. The TG module generates the texture using Markov Random Fields (MRF). The parameters of the MRF are calculated from the Tamura descriptors and then the textures are generated. As a result of this generation process a set of textures is presented to the user. If the user considers that one of the generated textures satisfy his query, the process finishes here. If not, the user enters into an iterative process. The iterative generation of the textures is implemented using interactive evolutionary computation (EPA module). It should be pointed out that the subjective or humanlike texture description that the system accepts, was determined by a psychological study in texture perception, performed by co-workers of the authors, and that will be presented elsewhere.

Figure 1.

Block diagram of TEXRET


In the next two sections the modules dealing with the generation of textures are presented.

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