Classification of Natural Textures using Rule Based Rank Matrix
1K.S.R.K.Sarma, Research Scholar, JNTUA, Assistant Professor in CSE Department at Vidya Jyothi Institute of Technology (Autonomous), Hyderabad, Telangana, India.
2M.Ussenaiah, Assistant Professor, Dept. of Computer Science, Vikram Simhapuri University, Nellore, Andhra Pradesh, India
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 2660-2666 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8839019320/2020©BEIESP | DOI: 10.35940/ijitee.C8839.019320
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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: This paper derives a new frame work for the classification of natural textures based on gradient rank vectors derived on a 2 X 2 grid. This paper identified the ambiguity in deriving ranks when two or more positions of the grid possess the same value. To attend this ambiguity and without increasing the total number of rank vectors on d positions this paper derived a rule based rank vector frame work. This paper replaced the 2 X 2 grid with the column position of the Rule based Rank Word Matrix (RRWM). The range of column positions will be d! for d positions. This paper then divides RRW texture image, into a 3 X 3 grid and derives cross and diagonal rule based rank words. From this, the present paper derived Rule based Rank Word-Cross and Diagonal Texture Matrix (RRW-CDTM) and derives GLCM features for effective texture classification. The experimental results on various texture databases revels the classification accuracy of the proposed method. The proposed method is compared with the state of art local based approaches.
Keywords: Cross and Diagonal Units, GLCM Features, Gradient, Rank, 2 X 2 grid, 3 X 3 grid.
Scope of the Article: Classification