Feature Selection Based on Genetic Algorithm for CBIR

Zhao, Tianhong; Lu, Jianjiang; Zhang, Yafei; Xiao, Qi

doi:10.1109/cisp.2008.90

Cited by 18 publications

(4 citation statements)

References 12 publications

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“…Ekta and Hardeep [59] proposed the use of bayesian algorithm, as a supervised learning and a statistical method for classification, by reducing the noise from images. Some approaches combined low-level content and genetic algorithm for features optimization [60,61]. Others integrated the user intervention by selecting and marking images as relevant/irrelevant and the system will update the results.…”

Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that B Asma ElAdel the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

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Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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“…Heuristic methods have been extensively used in CBIR for optimizing feature selection methods and improving efficiency of classifiers. Two kinds of two-stage feature selection schemes for weight optimization and descriptor subset selection were proposed by Zhao et al (2008). The proposed technique used Binary GA and a coded GA. Faria et al (2010) explored the idea of introducing algorithms that combine information from various descriptors.…”

Section: Related Workmentioning

confidence: 99%

Improved cuckoo search with particle swarm optimization for classification of compressed images

Enireddy

Kumar

2015

Sadhana

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The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO)-Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.

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“…Genetic Algorithm [12] In Genetic Algorithm, feature vectors have been seen as the chromosome and the retrieval recall as the fitness, the implementation can be summarized as follows:…”

Section: Retrieval Systemmentioning

confidence: 99%

“…Detailed descriptions about them are given in the next two sections. In short-term relevance feedback, Query Reweighting Algorithm [10] , Support Vector Machines (SVM) [11] and Genetic Algorithm [12] are discussed here. A. Query Reweighting [10] In this algorithm, the feature vectors and feature vector components which have high likelihood and distinguish well between positive relevant images and negative relevant images need to be assigned larger weights during the similarity matching in feedback process.…”

Section: Retrieval Systemmentioning

confidence: 99%

A Relevance Feedback System for CBIR with Long-Term Learning

Huang

Yang

et al. 2010

2010 International Conference on Multimedia Information Networking and Security

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Relevance feedback has been developed to improve retrieval performance effectively in Content Based Image Retrieval (CBIR). This paper introduces a relevance feedback system for CBIR with both short-term relevance feedback and long-term learning. In short-term relevance feedback, query reweighting algorithm, support vector machines (SVM), and genetic algorithm are adopted. In long-term learning, the expanded-judging model with index table is used for analyzing the historical log data. Experimental results show that among short-term feedback algorithms, the SVM gets the best feedback results, and for the use of our proposed expandedjudging model in long-term learning, the recall of the retrieval system is improved more than 30% in average.

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Feature Selection Based on Genetic Algorithm for CBIR

Cited by 18 publications

References 12 publications

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

Improved cuckoo search with particle swarm optimization for classification of compressed images

A Relevance Feedback System for CBIR with Long-Term Learning

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