Similarity Images with Hierarchical Graph based on WordNet Thesaurus
Main Article Content
Abstract
- Image retrieval is active research topic in image processing field. There are many research groups that have attempted to improve new technique for better results. However, the model cannot specific enough for representing the meaning of images. In this paper, we present a novel technique of the similarity images on hieratical graph based on WordNet. The model is divided into 3 steps that included (1) prepared the image data (2) related the relationship on images and (3) evaluated with similarity measure. The experimental results indicate that our proposed approach offers significant performance improvements in the interpretation of semantic image with the maximum of 85.7%
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
2. Ying Liua, Dengsheng Zhanga, , Guojun Lua, , Wei-Ying Mab,A survey of content-based image retrieval with high-level semantics, Pattern Recognition, Volume 40, Issue 1, January 2007, Pages 262–282.
3. Kevin Lin, Huei-Fang Yang, Kuan-Hsien Liu, Jen-Hao Hsiao, Chu-Song Chen Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search ACM International Conference on Multimedia Retrieval (ICMR), 2015.
4. Jia Li, James Z. Wang. “Automatic linguistic indexing of pictures by a statistical modeling approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25, 9, pp. 1075-1088.
5. Galleguillos C., Belongie S, Context Based Object Categorization: A Critical Survey. Computer Vision and Image Understanding (CVIU), Vol. 114, 2010, pp. 712-722.
6. Tie Hua Zhou, Ling Wang , and Keun Ho Ryu, Supporting Keyword Search for Image Retrieval with Integration of Probabilistic Annotation , 7,Sustainability 2015, 6303-6320.
7. Russell, B.C., Torralba, A., Murphy, K.P., and Freeman, W.T.. “LabelMe: a database and web-based tool for image annotation,” International Journal Computer Vision, vol.77, 2008.
8. L. Von Ahn, Liu, and M. Blum, “Peekaboom: a game for locating objects in images”, In: Proceedings SIGCHI conference on Human Factors in Computing Systems, 2006, pp. 55–64.
9. K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M.I. Jordan, “Matching words and pictures,” J. Mach. Learn. Rese., vol.3, pp. 1107–1135, 2003.
10. M. Johnson and R. Cipolla, “Improved image annotation and labeling through multi-label boosting,” in Brit. Machine Vision Conf., 2005.
11. Venkatesh N. Murthy, Subhransu Maji, R. Manmatha, Automatic Image Annotation using Deep Learning Representations, Proceeding ICMR '15 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 2015, pp. 603-606.
12. Patwardhan and Pedersen, “Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts,” Proceedings of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together, pp. 1-8, April 4, 2006, Trento, Italy.
13. Miller, George A. “WordNet: An on-line lexical database,” International Journal of Lexicography, Vol. 3, 1990, pp. 235–312.
14. I. Simon, N. Snavely, and S. Seitz, “Scene summarization for online image collections,” in IEEE Int. Conf. Computer Vision, 2007, pp. 1–8.
15. Y. Wu, J.-Y. Bouguet, A. Nefian, and I. Kozintsev, “Learning concept templates from web images to query personal image databases,”in IEEE Int. Conf. Multimedia and Expo, 2007, pp. 1986–1989.