A kind of feedback image retrieval algorithm based on PSO, Wavelet and subblock sorting thought

Wei, Kaiping; Lu, Tingwen; Wu, Bi; Sheng, Honghong

doi:10.1109/icfcc.2010.5497319

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…On the basis of Rocchio formula, Jing proposed the weight QPM algorithm [5]. However it does not take the query vector obtained previously into the feedback iterative process, which can make the query information loss.…”

Section: Introductionmentioning

confidence: 99%

Relevance Feedback Based on Particle Swarm Optimize Weight-Vector for Image Retrieval

Yang

2014

AMR

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As the physical meaning of components are different in feature vector, this paper presents a weight query vector based on QPM to represent the user’s true intention more properly, and then proposes two RF frameworks to learn the weights for positives and negatives in the feedback process of CBIR by PSO. Experiments were conducted to validate the proposed frameworks based on color histogram weight-vector. The proposed frameworks were compared and outperformed four other relevance feedback methods regarding their efficiency and effectiveness, thanks to the fact that they can make full use of the features’ different importance.

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Section: Introductionmentioning

confidence: 99%

Relevance Feedback Based on Particle Swarm Optimize Weight-Vector for Image Retrieval

Yang

2014

AMR

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“…A novel method based on PSO algorithm was proposed by Ye et al for image retrieval [22]. Also, a new approach based on PSO and wavelet was proposed by Wei et al that PSO was employed to optimize the weights [23].…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization

Heidari¹,

Chalechale²

2016

ijacsa

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Abstract-Image retrieval system as a reliable tool can help people in reaching efficient use of digital image accumulation; also finding efficient methods for the retrieval of images is important. Color and texture descriptors are two basic features in image retrieval. In this paper, an approach is employed which represents a composition of color moments and texture features to extract low-level feature of an image. By assigning equal weights for different types of features, we can't obtain good results, but by applying different weights to each feature, this problem is solved. In this work, the weights are improved using a modified Particle Swarm Optimization (PSO) method for increasing average Precision of system. In fact, a novel method based on an evolutionary approach is presented and the motivation of this work is to enhance Precision of the retrieval system with an improved PSO algorithm. The average Precision of presented method using equally weighted features and optimal weighted features is 49.85% and 54.16%, respectively. 4.31% increase in the average Precision achieved by proposed technique can achieve higher recognition accuracy, and the search result is better after using PSO.

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Particle Swarm Programming-Based Interactive Content-Based Image Retrieval

Yang

Tian

et al. 2018

Advances in Multimedia Information Processing – PCM 2018

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A kind of feedback image retrieval algorithm based on PSO, Wavelet and subblock sorting thought

Cited by 4 publications

References 5 publications

Relevance Feedback Based on Particle Swarm Optimize Weight-Vector for Image Retrieval

Relevance Feedback Based on Particle Swarm Optimize Weight-Vector for Image Retrieval

An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization

Particle Swarm Programming-Based Interactive Content-Based Image Retrieval

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