Image Retrieval Using Relevance Feedback Model

Neethu George; Akhil Paulose; Stephin Rachel Thomas

doi:https://doi.org/10.14445/22315381/IJETT-V43P213

Research Article | Open Access | Download PDF

Volume 43 | Number 1 | Year 2017 | Article Id. IJETT-V43P213 | DOI : https://doi.org/10.14445/22315381/IJETT-V43P213

Image Retrieval Using Relevance Feedback Model

Neethu George, Akhil Paulose, Stephin Rachel Thomas

Citation :

Neethu George, Akhil Paulose, Stephin Rachel Thomas, "Image Retrieval Using Relevance Feedback Model," International Journal of Engineering Trends and Technology (IJETT), vol. 43, no. 1, pp. 79-82, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V43P213

Abstract

Content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images. This system proposes clustering based Relevance Feedback to achieve high effectiveness and efficiency. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, system makes use of the discovered navigation patterns and query refinement strategies. Usage of data mining techniques like Apriori algorithm, KNN approach, K-Means clustering not only improved the efficiency of the CBIR systems, but also improved the accuracy of the overall process. Content-based image retrieval with relevance feedback, based on the clustering algorithm is a novel approach.

Keywords

Clustering, Relevance Feedback, indexing, Content based image retrieval, Frequent itemset mining.

References

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