Edge Texture Analysis for Image Retrieval Application with Aid of Robust Object Recognition
Anagha Sudhakaran1, Manu Prasad2

1Ms. Anagha Sudhakaran, Pursuing MTech in Communication Engineering, Electronics & Communication Department, Vedavyasa Institute of Technology, Kozhikode, Kerala, India.
2Mr. Manu Prasad, Assistant Professor, Department of Electronics & Communication, AWH Engineering College, Kozhikode, Kerala, India.
Manuscript received on 06 March 2015 | Revised Manuscript received on 26 March 2015 | Manuscript Published on 30 March 2015 | PP: 7-10 | Volume-4 Issue-10, March 2015 | Retrieval Number: J19820341015/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: It is a new approach in extension with local binary pattern and ternary pattern called DRLBP and DRLTP. By using these methods, the category recognition system will be developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. The discriminative robust local binary pattern (DRLBP) and discriminative robust local ternary pattern (DRLTP) are used for different object texture and edge contour feature extraction process. It is robust to illumination and contrast variations as it only considers the signs of the pixel differences. The features retain the contrast information of image patterns. They contain both edge and texture information which is desirable for object recognition. The DRLBP discriminates an object like the object surface texture and the object shape formed by its boundary. The boundary often shows much higher contrast between the object and the background than the surface texture. Differentiating the boundary from the surface texture brings additional discriminatory information because the boundary contains the shape information. These features are useful to distinguish the maximum number of samples accurately and it is matched with already stored image samples for similar category classification. The simulated results will be shown that used DRLBP and DRLTP has better discriminatory power and recognition accuracy compared with prior approaches. Index Terms—
Keywords: Histograms of equivalent patterns (HEP), Local binary pattern (LBP), Local ternary pattern (LTP), Robust Local binary pattern (RLBP), Robust Local ternary pattern (RLTP)

Scope of the Article: Robotics