Work place: Department of Computer Science, Krishna University, Machilipatnam, A.P. India
Research Interests: Image Processing, Image and Sound Processing, Computer Networks, Image Compression, Pattern Recognition
Y. K. Sundara Krishna qualified in Ph.D. in Computer Science & Engineering from Osmania University, Hyderabad. Now, he is working as Professor in the Department of Computer Science, Krishna University, and Machilipatnam. His research interests are Mobile Computing, Service Oriented Architecture, image processing and having practical work experience in the areas of Computing Systems including Developing of Simulators for Distributed Dynamic Cellular Computing Systems, Applications of Embedded & Win32 clients, Maintenance of Multi-user System Software. Also he is working with International Telecommunications Union (ITU): Y. 2018 recommendation series Y: Global Information Infrastructure, Internet Protocol aspects and NGN.
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.[...] Read more.
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