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Multi-Relation Image Retrieval and Annotation based on Holistic Approach & Decision Tree
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 7 |
| Year of Publication: 2012 |
| Authors: Sarla More, Nishchol Mishra |
10.5120/4836-7096
|
Sarla More, Nishchol Mishra . Multi-Relation Image Retrieval and Annotation based on Holistic Approach & Decision Tree. International Journal of Computer Applications. 39, 7 ( February 2012), 39-44. DOI=10.5120/4836-7096
Abstract
This work of multi-relation image retrieval and annotation is based on the holistic approach and the decision tree. In this we have proposed that for the retrieval of similar images as that of query image Dominant color descriptor (DCD) is used, this descriptor uses the color feature for the retrieval of images. This creates the feature vector index. Test keywords are correlated with the feature vector index, the correlation is performed by multi class association to get the classes for processing on them. Classes which are not necessary are discarded using cross validation in the decision tree process Decision tree used to take the relevant classes and finally we calculate the Gain of feature vector and we get the retrieval of the images based on the query image with the associated keywords or annotation. This work has been implemented on MatLab 7.5 simulator.
References
- Ankur M. Teredesai, Muhammad A. Ahmad, Juveria Kanodia, Roger S. Gaborski, “CoMMA: a framework for integrated multimedia mining using multi-relational associations” Springer-Verlag London Ltd. 2005.
- O zge, O ztimur Karadag, Fatos¸ T. Yarman Vural, Member IEEE “HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach”, 2009 IEEE
- Manjunath T.N, Ravindra S Hegadi, Ravikumar GK, “A Survey on Multimedia Data Mining and Its Relevance Today”, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.11, November 2010 K. Elissa
- T. Sumathi, C.Lakshmi Devasena, and M.Hemalatha “An Overview of Automated Image Annotation Approaches” International Journal of Research and Reviews in Information Sciences Vol. 1, No. 1, March 2011
- Fuming Sun, Yong Ge, Dongxia Wang and Xueming Wang “A Collaborative Approach for Image Annotation” 2010 IEEE
- Dimitris K. Iakovidis, and Christos V. Smailis, “Efficient Semantically-Aware Annotation of Images”, 2011 IEEE
- Ja-Hwung Su, Chien-Li Chou, Ching-Yung Lin, and Vincent S. Tseng, Member, IEEE “Effective Semantic Annotation by Image-to-Concept Distribution Model” IEEE Transactions on multimedia, vol. 13, no. 3, june 2011
- Bo Li, Hong Li, Min Wu, Ping Li “Multi-label Classification based on Association Rules with Application to Scene Classification” 2008 IEEE
- Ran Li, YaFei Zhang, Zining Lu, Jianjiang Lu, Yulong Tian, “Technique of Image Retrieval based on Multi-label Image Annotation” 2010 IEEE
- Ritendra datta, dhiraj joshi, jia li, and james z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age”, ACM Computing Surveys, Vol. 40, No. 2, Article 5, Publication date: April 2008
- Ameesh Makadia, Vladimir Pavlovic, and Sanjiv Kumar, “A New Baseline for Image Annotation” Google Research, New York, NY, Rutgers University, Piscataway, NJ
- Peng Huang, Jiajun Bu, Chun Chen, Kangmiao Liu, Guang Qiu “Improve Image Annotation by Combining Multiple Models”, 2008 IEEE
- Jens-Rainer Ohm, Leszek Cieplinski, Heon Jun Kim, Santhana Krishnamachari, B. S. Manjunath, Dean S. Messing, Akio Yamada “The MPEG-7 Color Descriptors”
- Hong Shao, Yueshu Wu, Wencheng Cui, Jinxia Zhang “Image Retrieval Based on MPEG-7 Dominant Color Descriptor“, The 9th International Conference for Young Computer Scientists, 2008 IEEE
- Zhiping Shi & Xi Liu & Qingyong Li & Qing He & Zhongzhi Shi, “Extracting discriminative features for CBIR”, Springer Science Business Media, LLC 2011
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