Sketch-based Image Retrieval using Rotation-invariant Histograms of Oriented Gradients

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-49 Number-2
Year of Publication : 2017
Authors : Y.Jhansi, Dr.E.Sreenivasa Reddy
DOI :  10.14445/22312803/IJCTT-V49P118

MLA

Y.Jhansi, Dr.E.Sreenivasa Reddy "Sketch-based Image Retrieval using Rotation-invariant Histograms of Oriented Gradients". International Journal of Computer Trends and Technology (IJCTT) V49(2):121-124, July 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
This paper presents a novel approach for sketch-based image retrieval based on Rotation Invariant. The approach enables measuring the similarity between a full color image and a simple black and white sketched query. We present efficient feature representation method namely rotation-invariant histograms of oriented gradients (Ri-HOG) for image retrieval. Most of the existing HOG techniques are computed on a dense grid of uniformly-spaced cells and use overlapping local contrast of rectangular blocks for normalization. However, we implement annular spatial bins type cells and apply radial gradient to attain gradient binning invariance for feature extraction. In this way, it significantly improves HOG in regard to rotation invariant ability and feature descripting accuracy. In experiments, the proposed method is evaluated on wang datasets. The experimental results demonstrate that the proposed method is much more effective than many existing image feature descriptors for sketch based image retrieval.

References
[1] Lee, Y.J., Zitnick, C.L., Cohen, M.F.: Shadow draw: real-time user guidance for freehand drawing. In: ACM Transactions on Graphics (Proc. SIGGRAPH). vol. 30, p. 27. ACM (2011)
[2] Samy Ait-Aoudia, Ramdane Mahiou, Billel Benzaid, YACBIR: Yet another Content Based Image Retrieval system?, IEEE 2010
[3] Bozas B, Konstantinos K, Izquierdo E. Large scale sketch based image retrieval using patch hashing.Advances in Visual Computing; 2012. p.210–19.
[4] Eitz M. Hildebrand K. Boubekeur T, Alexa M . Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors In IEEE Transactions on Visualization and Computer Graphics 2011, 17(11), pp. 1624 – 1636.
[5] Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color image database-query by visual example. In: Proc. of the 11th IAPR International Conf. on Computer Vision and Applications, Conf. A: Pattern Recognition, pp. 530–533, 1992
[6] Chalechale, A., Naghdy, G., Mertins, A.: Sketch-based image matching using angular partitioning. IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans 35(1), 28–41, 2005
[7] Chen, J.; Nakashika, T.; Takiguchi, T.; and Ariki, Y. 2015. Content-based image retrieval using rotation-invariant histograms of oriented gradients. In The ACM, 443–446
[8] Dalal, N. and Triggs, B., “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, San Diego, CA, USA.
[9]. Jhansi Y, Reddy E S.,” A new approach for Sketch Based Image Retrieval using FISH SWARM optimization with the aid of Optimal score level fusion,” In Indian Journal of Science and Technology ,Vol 9,issue 41,November 2016,pp. 1-9
[10]. Takacs, G., Chandrasekhar, V., Tsai, S., Chen, D., Grzeszczuk, R., Girod, B.: Unified real-time tracking and recognition with rotation-invariant fast features. In: CVPR. (2010)
[11] Jhansi Y, Reddy E S. “A methodology for sketch based image retrieval based on score level fusion”, In International journal of computer Applications, 2015, 109(3), pp. 9-13.
[12] Jhansi Y, Reddy E S “An Efficient Sketch Based Image Retrieval using Cross-correlation”, IJCSIS, Vol 14. No 12, pp 445-451, Dec 2016.
[13] Rupinder kaur , Navleen kaur,”Content Based Image Mining Technique for Image Retrieval Using Optimized Hybrid Clustering”, IJCTT, Vol 11.NO 3,2014, pp 141-143

Keywords
Image retrieval, rotation, HOG.