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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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Architecture of the PSO-SVM-RF.
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fig2: Architecture of the PSO-SVM-RF.

Mentions: As shown in the Figure 1, PSO-SVM-RF process starts with getting query from the user to search the similar images from the image database. Based on the similarity rank, nearest images known as NFB are displayed to the user to obtain user feedback. The similarity between the query image and the database images is measured based on the minimum distance using image feature vector. The distance between the query image and the database images is computed using Manhattan distance which is a similarity measure technique. For displayed images, the user has to mark the relevant images. Subsequently two subsets are created and termed relevant and irrelevant images, where all the images marked by user are treated as relevant images while the rest of the images are labeled as irrelevant images. The relevant images are used to update the swarms of PSO for evolutionary process. The number of relevant images is updated through iterative process. Details of updating the relevant images are discussed in Section 3.3. After the evolutionary process, the output produced by the PSO is used to train the SVM. Finally, SVM will classify relevant and irrelevant images. Then NFB nearest images are displayed to the user to collect the first feedback. Architecture of the overall PSO-SVM-RF is described in Figure 2. Detailed mechanism of the PSO-SVM-RF is presented in the following sections.


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

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

Architecture of the PSO-SVM-RF.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Architecture of the PSO-SVM-RF.
Mentions: As shown in the Figure 1, PSO-SVM-RF process starts with getting query from the user to search the similar images from the image database. Based on the similarity rank, nearest images known as NFB are displayed to the user to obtain user feedback. The similarity between the query image and the database images is measured based on the minimum distance using image feature vector. The distance between the query image and the database images is computed using Manhattan distance which is a similarity measure technique. For displayed images, the user has to mark the relevant images. Subsequently two subsets are created and termed relevant and irrelevant images, where all the images marked by user are treated as relevant images while the rest of the images are labeled as irrelevant images. The relevant images are used to update the swarms of PSO for evolutionary process. The number of relevant images is updated through iterative process. Details of updating the relevant images are discussed in Section 3.3. After the evolutionary process, the output produced by the PSO is used to train the SVM. Finally, SVM will classify relevant and irrelevant images. Then NFB nearest images are displayed to the user to collect the first feedback. Architecture of the overall PSO-SVM-RF is described in Figure 2. Detailed mechanism of the PSO-SVM-RF is presented in the following sections.

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