Connected component method to find components of GMM in image retrieval

dc.contributor.author	Methre, Renuka
dc.contributor.author	Bhagvati, Chakravarthy
dc.date.accessioned	2022-03-27T05:55:00Z
dc.date.available	2022-03-27T05:55:00Z
dc.date.issued	2010-12-01
dc.description.abstract	One of rudimentary problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. Relevance feedback (RF) is used to reduced this gap. In this paper Gausssian Mixture Model(GMM) is used to model the target distribution of query. Here a novel idea to estimate the components of GMM is proposed based on Connected component analysis. Connected component analysis uses positive and negative labeled examples obtained from relevance feedback to estimate the number of components of GMM. The retrieval performance of the proposed method is compared with MARS, and MindReader to show the efficiency using Wang and 5000 corel database. © 2010 IEEE.
dc.identifier.citation	Proceedings - 2010 International Conference on Computational Intelligence and Communication Networks, CICN 2010
dc.identifier.uri	10.1109/CICN.2010.21
dc.identifier.uri	https://ieeexplore.ieee.org/document/5701936
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/8758
dc.subject	CBIR
dc.subject	Connected component
dc.subject	GMM
dc.subject	Relevance feedback
dc.title	Connected component method to find components of GMM in image retrieval
dc.type	Conference Proceeding. Conference Paper
dspace.entity.type

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