Date of Publication :20th March 2018
Abstract: The system proposes new approach in extension with local color and Fast curvelet transform and entropy measurement in RGB Space. Discrete curvelet transform is one of the most powerful approaches in capturing edge curves in an image. The project presents the robust object recognition using texture and directional feature extraction. The system proposes texture descriptors such as Fast Discrete Curvelet Transform (FDCT) based entropy feature which represents better texture and edges and Local Directional Pattern (LDP) which provides textural details about all eight directions. By using these methods, the category recognition system will be developed for application to image retrieval which proves Low computational complexity and high compatibility.The tests are performed more than 12 seat stamp regular scene and shading surface picture databases, for example, Corel-1k, MIT-VisTex, USPTex, Hued Brodatz, et cetera.
Reference :
-
- F. Long, H. J. Zhang and D. D. Feng, Fundamentals of Content-based Image Retrieval, In Multimedia Information Retrieval and Management, D. Feng Eds, Springer,2003.
- Elhabiby M, Elsharkawy A, El-Sheimy N. Second Generation Curvelet Transform Vs Wavelet Transforms and Canny Edge Detector for Edge Detection from World View-2 data, Int. J. Comput Sci. & Eng Survey (IJCSES) 2012; 3: 1-13
- Jeena Jacob, K. G. Srinivasagan, and K. Jeya Priya, Local oppugnant texture pattern for image retrieval system. Pattern RecognitionLetters, vol.42 pp.72-78, 2014.
- D. Zhang, M. M. Islam, G. Lu, and I. J. Sumana. Rotation Invariant Curvelet Features for Region Based Image Retrieval. International Journal of Computer Vision, vol.2 pp.187-201, 2012.
- Fan-Hui Kong, and Harbin. Image retrieval using both color and texture. Proceedings in IEEE International Conference on Machine Learning Cybernatics, vol.4 pp. 2228 – 2232, 2009.
- Manimala Sinha, and K. Hemachandran. Content Based Image Retrieval using color and texture. International Journal of Signal and Image Processing, vol.3 pp. 39-56, 2012.
- M. Levine. Vision in Man and Machine. McGraw-Hill 1985.
- Jing Yi Tou, Yong Haur Tay, and Phooi Yee Lau. One dimensional Greylevel Co-occurrence Matrices for texture classification, IEEE International symposium on. Information Technology, pp.1-6, 2008.
- M. Subrahmanyan Q. M. Jonathan, Wu, R. P. Maheshwari, and R. Balasubramanian. Modified color motif co-occurrence matrix for image indexing and retrieval. Computer. Electrical. Engineering, pp.1 762-774, 2013.