A. Srinivasa Rao

Work place: Krishna University, Machilipatnam, India

E-mail: akella.srinivas08@gmail.com


Research Interests: Information-Theoretic Security, Information Theory, Information Systems, Computer Science & Information Technology


A. Srinivasa Rao He is presently working as Principal (i/c)& Assoc. professor, Head; C.Sc.Dept Montessori Siva Sivani institute of Science & Technology College of Engineering Mylavaram. He is pursuing Ph.d from Krishna University, Machalipatnam under the guidance of Prof.V.Venkata Krishna. He got 19 years of teaching experience and taught various courses to UG and PG programs. He served industry for 6 years as free-lance programmer. He is Organizer, Advisory member for various National, International Conferences in the field of Information Technology. Member in various Professional Bodies like ISPACE, ASCAP, IACSIT etc.

Author Articles
Dual Transition Uniform Lbp Matrix for Efficient Image Retrieval

By V.Vijaya Kumar A. Srinivasa Rao Y.K. Sundara Krishna

DOI: https://doi.org/10.5815/ijigsp.2015.08.06, Pub. Date: 8 Jul. 2015

Texture image retrieval plays a significant and important role in these days, especially in the era of big-data. The big-data is mainly represented by unstructured data like images, videos and messages etc. Efficient methods of image retrieval that reduces the complexity of the existing methods is need for the big-data era. The present paper proposes a new method of texture retrieval based on local binary pattern (LBP) approach. One of the main disadvantages of LBP is, it generates 256 different patterns on a 3x3 neighborhood and a method based on this for retrieval needs 256 comparisons which is very tedious and complex. The retrieval methods based on uniform LBP's which consists of 59 different patterns of LBP is also complex in nature. To overcome this, the present paper divided LBP into dual LBP's consisting four pixels. The present paper based on this dual LBP derived a 2-dimensional dual uniform LBP matrix (DULBPM) that contains only four entries. The texture image retrieval is performed using these four entries of DULBPM. The proposed method is evaluated on the animal fur, car, leaf and rubber textures. 

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