An Alternate Approach for Designing a Domain Specific Image Search Prototype Using Histogram


Everyone knows that thousand of words are represented by a single image. As a result, image search has become a very popular mechanism for the Web-searchers. Image search means, the search results are produced by the search engine should be a set of images along with their Web-page Unified Resource Locator (URL). Now Web-searcher can perform two types of image search, they are “Text to Image” and “Image to Image” search. In “Text to Image” search, search query should be a text. Based on the input text data, system will generate a set of images along with their Web-page URL as an output. On the other hand, in “Image to Image” search, search query should be an image and based on this image, system will generate a set of images along with their Web-page URL as an output. According to the current scenarios, “Text to Image” search mechanism always not returns perfect result. It matches the text data and then displays the corresponding images as an output, which is not always perfect. To resolve this problem, Web researchers have introduced the “Image to Image” search mechanism. In this paper, we have also proposed an alternate approach of “Image to Image” search mechanism using Histogram.

Share and Cite:

S. Sinha, R. Dattagupta and D. Mukhopadhyay, "An Alternate Approach for Designing a Domain Specific Image Search Prototype Using Histogram," Journal of Software Engineering and Applications, Vol. 6 No. 3, 2013, pp. 131-139. doi: 10.4236/jsea.2013.63017.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] T. Berners-Lee and M. Fischetti, “Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor,” HarperBusiness, New York, 1999.
[2] W. Willinger, R. Govindan, S. Jamin, V. Paxson and S. Shenker, “Scaling Phenomena in the Internet,” Proceedings of the National Academy of Sciences, New York, 19 February 2002, pp. 2573-2580.
[3] J. J. Rehmeyer, “Mapping a Medusa: The Internet Spreads Its Tentacles,” Science News, Vol. 171, No. 25, 2007, pp. 387-388. doi:10.1002/scin.2007.5591712503
[4] B. M. Leiner, V. G. Cerf, D. D. Clark, R. E. Kahn, L. Kleinrock, D. C. Lynch, J. Postel, L. G. Roberts and S. Wolff, “A Brief History of Internet,” ACM Computer Communication, Vol. 35, No. 1, 2009, pp. 22-31. doi:10.1145/1629607.1629613
[5] X. Wang, J. Wu and H. Yang, “Robust Image Retrieval Based on Color Histogram of Local Feature Regions,” Springer, Berlin, 2009.
[6] C. L. Novak and S. A. Shafer, “Anatomy of a Color Histogram,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign, 15-18 June 1992, pp. 599-605. doi:10.1109/CVPR.1992.223129
[7] J. R. Smith, “Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression,” Ph.D. Thesis, Columbia University, New York, 1997.
[8] E. A. Bashkov and N. S. Kostyukova, “Effectiveness Estimation of Image Retrieval by 2D Color Histogram,” Journal of Automation and Information Sciences, Vol. 8, No. 11, 2006, pp. 74-80.
[9] E. A. Bashkov and N. S. Shozda, “Content-Based Image Retrieval Using Color Histogram Correlation,” Graphicon proceedings, Nizhny Novgorod, 16-21 September 2002, pp. 458-461.
[10] L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms,” IEEE Transactions on Image Processing, Vol. 2, No. 2, 1993, pp. 176-201.
[11] D. Mukhopadhyay, A. Biswas and S. Sinha, “A New Approach to Design Domain Specific Ontology Based Web Crawler,” The 10th International Conference on Information Technology, Orissa, 17-20 December 2007, pp. 289-291.
[12] S. Chakrabarti, M. Berg and B. E. Dom, “Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery,” Proceedings of the 8th International World Wide Web Conference, Toronto, 11-14 May 1999, pp. 545-562.
[13] D. Bergmark, C. Lagoze and A. Sbityakov, “Focused Crawls, Tunneling, and Digital Libraries,” Proceedings of the European Conference on Digital Libraries, Vol. 2458, Rome, 16-18 September 2002, pp. 91-106.
[14] M. Diligenti, F. Coetzee, S. Lawrence, C. L. Giles and M. Gori, “Focused Crawling Using Context Graphs,” The 26th International Conference on Very Large Databases, Cairo, 10-14 September 2000, pp. 527-534.
[15] N. Tyagi and D. Gupta, “A Novel Architecture for Domain Specific Parallel Crawler,” Indian Journal of Computer Science and Engineering, Vol. 1 No. 1, 2008, pp. 44-53.
[16] A. Kundu, R. D. Dattagupta and D. Mukhopadhyay, “Mining the Web with Hierarchical Crawlers—A Resource Sharing Based Crawling Approach,” International Journal on Intelligent Information and Database Systems, Vol. 3, No. 1, 2009, pp. 90-106. doi:10.1504/IJIIDS.2009.023040
[17] A. Kundu, R. Dutta, D. Mukhopadhyay and Y. C. Kim, “A Hierarchical Web Page Crawler for Crawling the Internet Faster,” Proceedings of the International Conference on Electronics and Information Technology Convergence, Republic of Korea, 20 December 2006, pp. 61-67.
[18] D. Mukhopadhyay, S. Mukherjee, S. Ghosh, S. Kar and Y. C. Kim, “Architecture of A Scalable Dynamic Parallel Web Crawler with High Speed Downloadable Capability for a Web Search Engine,” Proceedings of the 6th International Workshop, Republic of Korea, 20 November 2006, pp. 103-108.
[19] A. Gangemi, R. Navigli and P. Velardi, “The OntoWordNet Project: Extension and Axiomatization of Conceptual Relations in WordNet,” Proceedings of International Conference on Ontologies, Databases and Applications of Semantics, Catania, 3-7 November 2003, pp. 820-838.
[20] D. N. Antonio, M. Michele and N. Roberto, “A Software Engineering Approach to Ontology Building,” Information Systems, Vol. 34, No. 2, 2009, pp. 258-275. doi:10.1016/
[21] T. Gruber, “Toward Principles for the Design of Ontologies Used for Knowledge Sharing,” International Journal of Human-Computer Studies, Vol. 43, No. 5-6, 1995, pp. 907-928. doi:10.1006/ijhc.1995.1081
[22] M. Ehrig and A. Maedche, “Ontology-Focused Crawling of Web Documents,” Proceedings of the ACM Symposium on Applied Computing, Melbourne, 9-12 March 2003, pp. 1174-1178.
[23] B. Luo, X. G. Wang and X. O. Tang, “World Wide Web Based Image Search Engine Using Text and Image Content Features,” Proceedings of SPIE Electronic Imaging, Vol. 5018, Santa Clara, 20 January 2003, pp. 123-130.
[24] Z. Su, H. Zhang, S. Li and S. Ma, “Relevance Feedback Content-Based Image Retrieval: Bayesian Framework, Feature,” IEEE Transactions on Image Processing, Vol. 12, No. 8, 2003, pp. 924-937.
[25] K. P. Yee, K. Swearingen, K. Li and M. Hearst, “Faceted Metadata for Image Search and Browsing,” Proceedings of the ACM Digital Library, Fort Lauderdale, 5-10 April 2003, pp. 401-408.
[26] C. W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Pektovic, P. Yanker, C. Faloutsos and G. Taubin, “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape,” Proceedings of Storage and Retrieval for Image and Video Databases, San Jose, 31 January 1993, pp. 173-187.
[27] D. N. D. Harini and D. L. Bhaskari, “Image Retrieval System Based on Feature Extraction and Relevance Feedback,” Proceedings of the CUBE International Information Technology Conference, Pune, 3-5 September 2012, pp.69-73.
[28] A. K. Jain and A. Vailaya, “Image Retrieval Using Color and Shape,” Pattern Recognition, Vol. 29, No. 8, 1996, pp. 1233-1244.
[29] D. Patra and J. Mridula, “Featured Based Segmentation of Color Textured Images Using GLCM and Markov Random Field Model,” World Academy of Science, Engineering and Technology, Vol. 53, No. 5, 2011, pp. 108-113.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.