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Stochastic optimized relevance feedback particle swarm optimization for content based image retrieval.

Imran M, Hashim R, Noor Elaiza AK, Irtaza A - ScientificWorldJournal (2014)

Bottom Line: To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully.The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals.This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

View Article: PubMed Central - PubMed

Affiliation: Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, Malaysia.

ABSTRACT
One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

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Flow chart of the proposed system.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: Flow chart of the proposed system.

Mentions: As mentioned earlier, this study has focused on developing a new approach for enhancing the performance of CBIR using RF by integrating PSO and SVM named as PSO-SVM-RF. It consists of three processes as information gathering from user, swarms updating, and training of SVM. The flow chart of PSO-SVM-RF development is illustrated in Figure 1.


Stochastic optimized relevance feedback particle swarm optimization for content based image retrieval.

Imran M, Hashim R, Noor Elaiza AK, Irtaza A - ScientificWorldJournal (2014)

Flow chart of the proposed system.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4121098&req=5

fig1: Flow chart of the proposed system.
Mentions: As mentioned earlier, this study has focused on developing a new approach for enhancing the performance of CBIR using RF by integrating PSO and SVM named as PSO-SVM-RF. It consists of three processes as information gathering from user, swarms updating, and training of SVM. The flow chart of PSO-SVM-RF development is illustrated in Figure 1.

Bottom Line: To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully.The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals.This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

View Article: PubMed Central - PubMed

Affiliation: Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, Malaysia.

ABSTRACT
One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

Show MeSH