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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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Performance of the proposed PSO-SVM-RF compared against the representative of existing algorithms, that is, semi-BDEE, MBA, BDA, KBMCM, and ABRSVM. All algorithms are evaluated over nine RF iterations.
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fig3: Performance of the proposed PSO-SVM-RF compared against the representative of existing algorithms, that is, semi-BDEE, MBA, BDA, KBMCM, and ABRSVM. All algorithms are evaluated over nine RF iterations.

Mentions: The performance of PSO-SVM-RF is compared with the semi-BDEE [39], DBA [40], ABRSVM [13], MBA [41], and kernel biased marginal convex machine (KBMCM) [1]. For comparison purpose the results of the above-mentioned CBIR techniques with similar experimental setup as of PSO-SVM-RF are adopted from Bian and Tao [39] as shown in Figure 3. From Figure 3, it is perceived that average precision of PSO-SVM-RF is similar to semi-BDEE for top 10, 20, and 30 retrievals and is better than the other techniques. For top 40 retrievals, precision is achieved higher than 0.9 which is higher than all other techniques. In case of 50, 60, 70, 80, 90, and 100 top retrievals the average precision achieved by PSO-SVM-RF is 0.9 while the maximum average precision by other techniques is 0.71, 0.64, 0.59, 0.55, 0.5, and 0.48, respectively. These results highlight that PSO-SVM-RF has consistently outperformed semi-BDEE, MBA, BDA, ABRSVM, and KBMCM in all iterations and all top retrievals. It has promising convergence in earlier iterations compared to all other techniques, which means that the user can achieve desired results in few iterations only. Furthermore, PSO-SVM-RF technique is also compared with PSO-RF [33] and comparison graph is presented in Figure 5. From Figure 5 it can be noticed that in each iteration PSO-SVM-RF has higher precision value than PSO-RF precision. This demonstrates that the pairing of SVM with PSO has made RF more robust and enhanced the accuracy of CBIR. Hence it can be concluded that the developed PSO-SVM-RF technique is a precise and efficient CBIR technique.


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

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

Performance of the proposed PSO-SVM-RF compared against the representative of existing algorithms, that is, semi-BDEE, MBA, BDA, KBMCM, and ABRSVM. All algorithms are evaluated over nine RF iterations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Performance of the proposed PSO-SVM-RF compared against the representative of existing algorithms, that is, semi-BDEE, MBA, BDA, KBMCM, and ABRSVM. All algorithms are evaluated over nine RF iterations.
Mentions: The performance of PSO-SVM-RF is compared with the semi-BDEE [39], DBA [40], ABRSVM [13], MBA [41], and kernel biased marginal convex machine (KBMCM) [1]. For comparison purpose the results of the above-mentioned CBIR techniques with similar experimental setup as of PSO-SVM-RF are adopted from Bian and Tao [39] as shown in Figure 3. From Figure 3, it is perceived that average precision of PSO-SVM-RF is similar to semi-BDEE for top 10, 20, and 30 retrievals and is better than the other techniques. For top 40 retrievals, precision is achieved higher than 0.9 which is higher than all other techniques. In case of 50, 60, 70, 80, 90, and 100 top retrievals the average precision achieved by PSO-SVM-RF is 0.9 while the maximum average precision by other techniques is 0.71, 0.64, 0.59, 0.55, 0.5, and 0.48, respectively. These results highlight that PSO-SVM-RF has consistently outperformed semi-BDEE, MBA, BDA, ABRSVM, and KBMCM in all iterations and all top retrievals. It has promising convergence in earlier iterations compared to all other techniques, which means that the user can achieve desired results in few iterations only. Furthermore, PSO-SVM-RF technique is also compared with PSO-RF [33] and comparison graph is presented in Figure 5. From Figure 5 it can be noticed that in each iteration PSO-SVM-RF has higher precision value than PSO-RF precision. This demonstrates that the pairing of SVM with PSO has made RF more robust and enhanced the accuracy of CBIR. Hence it can be concluded that the developed PSO-SVM-RF technique is a precise and efficient CBIR technique.

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