Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

An Efficient Approach towards Satellite Image Retrieval using Semantic Mining with Hashing

Linda Mary John, Kiran A. Bhandari. Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Linda Mary John, Kiran A. Bhandari
10.5120/ijais2016451592
Download full text
  1. Linda Mary John and Kiran A Bhandari. An Efficient Approach towards Satellite Image Retrieval using Semantic Mining with Hashing. International Journal of Applied Information Systems 11(3):50-54, August 2016. URL, DOI BibTeX

    @article{10.5120/ijais2016451592,
    	author = "Linda Mary John and Kiran A. Bhandari",
    	title = "An Efficient Approach towards Satellite Image Retrieval using Semantic Mining with Hashing",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2016",
    	volume = 11,
    	number = 3,
    	month = "Aug",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "50-54",
    	numpages = 5,
    	url = "http://www.ijais.org/archives/volume11/number3/931-2016451592",
    	doi = "10.5120/ijais2016451592",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

Satellite images have gained a wide popularity in the field of content-based image retrieval. A massive amount of these images are collected every year due to the high availability of satellites and computer technologies. However, extracting user-specific content from these images still remains a primary concern due to the presence of semantic gap. This limits the capabilities of CBIR. Therefore, an effective and efficient method is required for image retrieval. This paper puts forward a semantic-based image retrieval approach along with the advantages of hashing for better feature extraction and precise retrieval. Hashing accelerates the quality of similarity search among images by generating unique image-hash codes .This approach also aims to scale down the problems related to semantic gap for better retrieval results.

Reference

  1. Mohammed Alkhawlani, Mohammed Elmogy, Hazem El Bakry: “Text-based, Content-based, and Semantic-based Image Retrievals: A Survey”, International Journal of Computer and Information Technology Volume 04 – Issue 01, January 2015 .
  2. Hui Hui Wang, Dzulkifli Mohamad, N.A Ismail:“ Image Retrieval: Techniques, Challenge, and Trend”, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:3, No:12, 2009.
  3. Nancy Goyal , Navdeep Singh:“ A Review on Different Content Based Image Retrieval Techniques Using High Level Semantic Feature”, International Journal of Innovative Research in Computer and Communication Engineering Vol. 2, Issue 7, July 2014
  4. Nikita Upadhyaya and Manish Dixit:“ A Review: Relating Low Level Features to High Level Semantics in CBIR ”,International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9, No.3 (2016).
  5. Ruksanamol T.A. and Femithamol A. M: “Class Based Image Search with Hash Codes”, International Journal of Innovation Research & Development Vol 3 Issue 9, September 2014.
  6. Shijun Xiang and Jianquan Yang:“ Block-based image hashing with restricted blocking strategy for rotational robustnes”, EURASIP Journal on Advances in Signal Processing ,2012.
  7. Y. Mu, J. Shen, and S. Yan:“ Weakly-supervised hashing in kernel space”, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010, pp. 3344–3351.
  8. D. Zhang, J. Wang, D. Cai, and J. Lu,“Self-taught hashing for fast similarity search,” in Proc. Conf. Advances in Neural Information Processing Systems, 2004, pp. 1–8.
  9. J. Wang, S. Kumar, and S. Chang, “Semi-supervised hashing for scalable image retrieval,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010, pp. 3424– 3431
  10. Marie Liénou, Henri Maître and Mihai Datcu, “Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation,” in IEEE Geoscience And Remote Sensing Letters, Vol. 7, No. 1, January 2010.
  11. P. Sumathi , R. Manickachezian:“ Semantic-Based Web Mining For Image Retrieval Using Enhanced Support Vector Machine”,International Journal of Applied Engineering Research ,Volume 11, Number 5 (2016) pp 3276-3281.
  12. Dr. M.V Siva Prasad, P. Sandeep Reddy, K. Krishna Reddy, “Mining User Queries with Markov Chain: Application to Content Based Image Retrieval System,” International Journal of P2P Network Trends and Technology (IJPTT) – Volume 8 – May 2014.
  13. Vlado Kitanovski ,Dimitar Taskovski, Sofija Bogdanova, “Combined Hashing/Watermarking Method for Image Authentication”, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:1, No:6, 2007.

Keywords

Semantic mining, Hashing, Hash codes, Semantic gap, Feature extraction, Semantic-based image retrieval, Precision, Recall.