Work place: Anurag Group of Institutions (Autonomous), Hyderabad,India
Research Interests: Engineering, Computational Engineering, Computational Science and Engineering
Mr. A.Obulesu is working as Asst.Prof. at Anurag Group of Institutions (AGI) (Autonomous), Hyderabad and graduated in B.Tech from Nagarjuna Institute of Technology, Vijayawada which is affiliated to JNTU Hyderabad in 2003. He received Masters Degree in M.Tech. from Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal which is affiliated to JNT University, Hyderabad, in 2006. At present he is pursuing Ph.D. from JNTU Kakinada in Image Processing under the guidance of Dr.Vakulabharanam Vijaya Kumar, Director - Centre for Advanced Computational Research (CACR) of Anurag Group of Institutions (Autonomous), Hyderabad and an active member in CACR. He has published 15 research papers in various National, International journals and conferences proceedings.
DOI: https://doi.org/10.5815/ijigsp.2018.06.07, Pub. Date: 8 Jun. 2018
We present a new technique for content based image retrieval by deriving a Local motif pattern (LMP) code co-occurrence matrix (LMP-CM). This paper divides the image into 2 x 2 grids. On each 2 x 2 grid two different Peano scan motif (PSM) indexes are derived, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. From these two different PSM indexes, this paper derived a unique LMP code for each 2 x 2 grid, ranges from 0 to 35. Each PSM minimizes the local gradient while traversing the 2 x 2 grid. A co-occurrence matrix is derived on LMP code and Grey level co-occurrence features are derived for efficient image retrieval. This paper is an extension of our previous MMCM approach . Experimental results on popular databases reveal an improvement in retrieval rate than existing methods.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2018.04.07, Pub. Date: 8 Apr. 2018
In this paper, two extended versions of motif co-occurrence matrices (MCM) are derived and concatenated for efficient content-based image retrieval (CBIR). This paper divides the image into 2 x 2 grids. Each 2 x 2 grid is replaced with two different Peano scan motif (PSM) indexes, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. This transforms the entire image into two different images and co-occurrence matrices are derived on these two transformed images: the first one is named as “motif co-occurrence matrix initiated from top left most pixel (MCMTL)” and second one is named as “motif co-occurrence matrix initiated from bottom right most pixel (MCMBR)”. The proposed method concatenates the feature vectors of MCMTL and MCMBR and derives multi motif co-occurrence matrix (MMCM) features. This paper carried out investigation on image databases i.e. Corel-1k, Corel-10k, MIT-VisTex, Brodtaz, and CMU-PIE and the results are compared with other well-known CBIR methods. The results indicate the efficacy of the proposed MMCM than the other methods and especially on MCM  method.[...] Read more.
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