Feature Selection for Image Retrieval based on Genetic Algorithm
DOI:
https://doi.org/10.9781/ijimai.2016.423Keywords:
Kmeans, Genetic Algorithms, Clustering, Feature ExtractionAbstract
This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.Downloads
References
[1] T. Hilda and Dr. R.R. Rajalaxmi, “Effective feature selection for supervised learning using Genetic Algorithm”, IEEE sponsored 2’nd International conference of electronics and communication, 2015 ISSN 4788-7225.
[2] Keyuri M. Zinzuvadia, Prof. Keyur N.Brahmbhatt, Prof. Bhavesh A. Tanawala, “A survey on Feature Based Image Retrieval using classification and Relevance Feedback technique”, International Journal of Innovative Research in Computer and Communication engineering (IJIRCCE), volume: 03, Issues: 01, 2015, pages: 508-513.
[3] Slimane Benloucif and Bachir Bouchehan,”Impact of feature selection on the performance of content- based image retrieval”, IEEE transaction 2014, ISSN 4799-7508.
[4] Lakshmi P.S, Thusnavis Bella Mary, A.Vasuki, Kolcharam Yadhul, Ansa Saju,“Image retrieval based on feature selection method” International Journal of Research in Engineering and Technology (IJRET), volume: 03, Issues: 07, 2014, pages: 34-38.
[5] Kommineni Jenni, Satria Mandala, “Pre-processing Image Database for Efficient content Based Image Retrieval”, International conference on Advances in Computing, Communications and Informatics, IEEE, Sept2014, pages: 968-972.
[6] C.V. Rashmi, Sk.Sjida Praveen and G. Prathibha, “A Novel Image Retrieval System Using ACO and Relevance Feedback” International Journal of Advanced Research in Computer science and Software Engineering (IJARCSSE), volume: 4, Issues: 7, 2014, pages: 422-427.
[7] Mit Patel, Kanu Patel, Keyur Bhrahmbhatt, “Feature based Image retrieval based on clustering, classification techniques using low level image features”, International Journal of Advance Engineering and Reasearch Development (IJAERD), volume: 1, issue:5, 2014, pages: 1-9.
[8] Ramandeep Kaur and Ashok Kumar Bathla, “Enhanced Content-Based Image Retrieval Using Cuckoo Search Algorithm”, International Journal of Advanced Research in Computer science and Software Engineering (IJARCSSE),volume; 4, Issue: 9, 2014, pages: 233-241.
[9] P.K.Bhargavi, S.Bhuvana, and Dr.R.Radhakrishnan, “A Novel Content Based Image Retrieval Model Based on the Most Relevant Features Using Particle Swarm Optimization”, Journal of Global Research in Computer Science (JGRCS), vol.: 4, 2013, pages: 25-30.
[10] Annesha Malakar, Joydeep Mukherjee, “Image Clustering using Color Moments, Histogram, Edge and K-means Clustering”, International Journal of Science and Research(IJSR), vol.1, 2013, pages: 532-537.
[11] Afifi A.J., Ashour W.M., “Content-Based Image Retrieval Using Invariant Color and Texture Features”, IEEE Int. Conference on Digital Image Computing techniques and Applications, 2012, pp. 1-6.
[12] Manimala Singha and K.Hemachandran, “Content Based Image Retrieval using Color and Texture”, Signal & Image Processing: An International Journal (SIPIJ), Vol.3, No.1, February 2012, pp. 234-237.
[13] Jayant Mishra, Anubhav Sharma and Kapil Chaturvedi, “An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance Feedback”, International Journal of Managing Information Technology (IJMIT) Vol.3, No.2, May 2011, pages: 10-16.
[14] L.Ladha, T.Deepa, “Feature Selection Methods And Algorithms”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 5 May 2011.
[15] Rajshree S. Dubey, Rajnish Choubey, Jor Bhattacharjee, “Multi feature Content Based Image Retrieval”, International Journal on Computer Science and Engineering (IJCSE), volume: 2, No. 06, 2010, pages: 2145-2149.
[16] Jianhua Wu, Zhaorong Wei, Youli Chang ,“Color and Texture Feature For Content based image retrieval”, International Journal of Digital Content Technology and its Applications,2010.
[17] D. Joshi, R. Datta, J. Li, and J.Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age”, ACM Comput. Surv., April-2008.
[18] Tianzhong Zhao, Jianjiang Lu, Yafei Zhang1, Qi Xiao, “Feature Selection based on Genetic Algorithm for CBIR”, Congress on Image and Signal processing, IEEE, volume: 02, May-2008, pages: 495-499.
[19] Jonathan E. Rowe, “Genetic Algorithm theory”, ACM GECCO ‘07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, July-2007.
[20] Ke-jia Chen, Zhi-Hua Zhou, Hong-Bin Dai, “Enhancing relevance feedback in image retrieval using unlabeled data”, ACM trans. on Information Systems, April-2006.
Downloads
Published
-
Abstract35
-
PDF13
Submit Article
Information
