TY - JOUR ID - 2027 TI - Relevance Feedback-based Image Retrieval using Particle Swarm Optimization JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Jafarinejad, F. AU - Farzbood, R. AD - Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran. AD - Faculty of Computer Engineering, Shahid Beheshti University, Tehran, Iran. Y1 - 2021 PY - 2021 VL - 9 IS - 2 SP - 245 EP - 257 KW - Image Retrieval KW - Swarm Optimization Algorithm KW - Relevance Feedback KW - image classification DO - 10.22044/jadm.2020.9014.2037 N2 - Image retrieval is a basic task in many content-based image systems. Achieving high precision, while maintaining computation time is very important in relevance feedback-based image retrieval systems. This paper establishes an analogy between this and the task of image classification. Therefore, in the image retrieval problem, we will obtain an optimized decision surface that separates dataset images into two categories of relevant/irrelevant images corresponding to the query image. This problem will be viewed and solved as an optimization problem using particle optimization algorithm. Although the particle swarm optimization (PSO) algorithm is widely used in the field of image retrieval, no one use it for directly feature weighting. Information extracted from user feedbacks will guide particles in order to find the optimal weights of various features of images (Color-, shape- or texture-based features). Fusion of these very non-homogenous features need a feature weighting algorithm that will take place by the help of PSO algorithm. Accordingly, an innovative fitness function is proposed to evaluate each particle’s position. Experimental results on Wang dataset and Corel-10k indicate that average precision of the proposed method is higher than other semi-automatic and automatic approaches. Moreover, the proposed method suggest a reduction in the computational complexity in comparison to other PSO-based image retrieval methods. UR - https://jad.shahroodut.ac.ir/article_2027.html L1 - https://jad.shahroodut.ac.ir/article_2027_da1b79e7823f3c8e5e13579f718bd750.pdf ER -