Research Article | OPEN ACCESS
Descriptor Trends in Texture Classification for Material Recognition
1Hayder Ayad, 2Mohammed Hasan Abdulameer, 3Loay E. George and 1Nidaa F. Hassan
1Department of Computer Science, University of Technology
2Department of computer science, Faculty of education for women, University of kufa
3Department Computer Science, College of Science, Al-Jaderia Campus, University of Baghdad, Baghdad, Iraq
Research Journal of Applied Sciences, Engineering and Technology 2015 10:1089-1101
Received: January 13, 2015 | Accepted: March 7, 2015 | Published: August 05, 2015
Abstract
Recent rapid growth in the demand for technology and image investigation in many applications, such as image retrieval systems and Visual Object Categorization (VOC), effective management of these applications has become crucial. Computer vision and its various applications are a primary focus of research. Content-based image retrieval is considered an extremely challenging issue and has remained an open research area. Obviously, the main challenge associated with this kind of research is the gap between the low-level features and the richness of the semantic concept of the human mind. This problem is called the semantic gap. Several methods have been proposed to increase the performance of the system and reduce the semantic gap. These proposed techniques make use of either global or local features or a combination of both global and local features on one side and the visual content and keyword-based retrieval on the other side. However, the aim of this study is to provide a constructive critique of the algorithms used in extracting the low-level features, either globally or locally or as a combination of both. In addition, it identifies the factors that can affect the low-level features that lead to the semantic gap. As well as, proposed a new framework to improve the Gabor filter and the edge histogram limitations. Finally, recommendations are made for the choice of the descriptors used to describe the low-level features, both locally and globally, depending on the area of limitations or drawbacks of the previous state-of-the-art research.
Keywords:
Combination feature, content-based image retrieval, global and local descriptors, SIFT descriptor, similarity measure,
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The authors have no competing interests.
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