The week's pick
Reseach Article
New Content based Image Retrieval using JEC and Lasso
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 21 |
| Year of Publication: 2020 |
| Authors: Samiksha Jain, Satish Pawar |
10.5120/ijca2020920730
|
Samiksha Jain, Satish Pawar . New Content based Image Retrieval using JEC and Lasso. International Journal of Computer Applications. 175, 21 ( Sep 2020), 5-10. DOI=10.5120/ijca2020920730
Abstract
In current scenario with growing technologies as well as enhancement in the digital world, it has found itself surrounded by a huge quantity of data or information. To handle such huge amount of data/images will often creates difficulties while retrieving the data or images efficiently. One feasible solution to overcome such difficulties is data retrieval technique. Image retrieval is the process of retrieving images from a large database of digital images dataset. proposed method is done by three primitive methods namely through color, shape and texture other term process data based on color ,size and texture. In this paper extract the image from database based on size texture and color. The technique by which used on proposed Wavelet transform Joint equal contribution and lasso. For proposed method is firstly take query image and extract feature by using DWT and other algorithm and then match that data with exiting database and find similarity data from the database. In this paper is analysis different author work and proposed novel method to get better retrieval rate.
References
- Ganar, Apurva N., C. S. Gode, and Sachin M. Jambhulkar. “Enhancement of image retrieval by using colour, texture and shape features” In Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on, pp. 251-255. IEEE, 2014.
- Pujari, Jagadeesh, and P. Hiremath. "Content based image retrieval based on color texture and shape features using image and its complement." International Journal of Computer Science and Security 1, no. 4 (2007): 25-35.
- Huang, Zhi-Chun, Patrick PK Chan, Wing WY Ng, and Daniel S. Yeung. “Content-based image retrieval using color moment and gabor texture feature” In Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, vol. 2, pp. 719-724. IEEE, 2010.
- Kekre, H. B., Ms Tanuja K. Sarode, and Sudeep D. Thepade. "Image retrieval using color-texture features from DCT on VQ codevectors obtained by Kekre’s fast codebook generation." ICGST-International Journal on Graphics, Vision and Image Processing (GVIP) 9, no. 5 (2009): 1-8.
- Bama, B. Sathya, S. Mohana Valli, S. Raju, and V. Abhai Kumar. "Content based leaf image retrieval (CBLIR) using shape, color and texture features." Indian Journal of Computer Science and Engineering 2, no. 2 (2011): 202-211.
- Iqbal, Kashif, Michael O. Odetayo, and Anne James. "Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics." Journal of Computer and System Sciences 78, no. 4 (2012): 1258-1277.
- Wang, Xiang-Yang, Yong-Jian Yu, and Hong-Ying Yang. "An effective image retrieval scheme using color, texture and shape features." Computer Standards & Interfaces 33, no. 1 (2011): 59-68.
- Murala, Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian. "Local tetra patterns: a new feature descriptor for content-based image retrieval." IEEE Transactions on Image Processing 21, no. 5 (2012): 2874-2886.
- Kekre, Dr HB, Sudeep D. Thepade, Priyadarshini Mukherjee, Shobhit Wadhwa, Miti Kakaiya, and Satyajit Singh. "Image retrieval with shape features extracted using gradient operators and slope magnitude technique with BTC." International Journal of Computer Applications 6, no. 8 (2010).
- Kekre, H. B., Sudeep D. Thepade, Tanuja K. Sarode, and Vashali Suryawanshi. “Image Retrieval using Texture Features extracted from GLCM, LBG and KPE” International Journal of Computer Theory and Engineering 2, no. 5 (2010): 695.
- Metzler, D., Manmatha, R.: An inference network approach to image retrieval. In: Image and Video Retrieval, Springer (2005) 42–50
- Hare, J.S., Lewisa, P.H., Enserb, P.G.B., Sandomb, C.J.: Mind the gap: Another look at the problem of the semantic gap in image retrieval. Multimedia Content, Analysis, Management and Retrieval (2006).
- Frome, A., Singer, Y., Sha, F., Malik., J.: Learning globally-consistent local distance functions for shape-based image retrieval and classification. In: Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil. (2007)
- Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Royal Statistical Soc., B 58 (1996) 267–288
- Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (2008)
- Mori, Y., Takahashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: First International Workshop on Multimedia Intelligent Storage and Retrieval Management (MISRM). (1999)
- Jin, R., Chai, J.Y., Si, L.: Effective automatic image annotation via a coherent language model and active learning. In: ACM Multimedia Conference. (2004) 892–899
- D. Zhang, Md. M. Islam, G. Lu, 2012. “A review on automatic image annotation techniques”, Pattern Recognition, vol. 45, no. 1,pp.346–362.
Index Terms
Keywords