An optimized feature selection CBIR technique using ANN

Mary, I. Thusnavis Bella; Vasuki, A.; Manimekalai, M.

doi:10.1109/iceeccot.2017.8284550

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Mary et al [ 42 ] presented a hybrid feature selection method based on a genetic algorithm. The feature set is a merger of color moments, entropy, energy, homogeneity, contrast, and feature descriptor.…”

Section: Related Workmentioning

confidence: 99%

Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval

et al. 2022

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The recent era has witnessed exponential growth in the production of multimedia data which initiates exploration and expansion of certain domains that will have an overwhelming impact on human society in near future. One of the domains explored in this article is content-based image retrieval (CBIR), in which images are mostly encoded using hand-crafted approaches that employ different descriptors and their fusions. Although utilization of these approaches has yielded outstanding results, their performance in terms of a semantic gap, computational cost, and appropriate fusion based on problem domain is still debatable. In this article, a novel CBIR method is proposed which is based on the transfer learning-based visual geometry group (VGG-19) method, genetic algorithm (GA), and extreme learning machine (ELM) classifier. In the proposed method, instead of using hand-crafted features extraction approaches, features are extracted automatically using a transfer learning-based VGG-19 model to consider both local and global information of an image for robust image retrieval. As deep features are of high dimension, the proposed method reduces the computational expense by passing the extracted features through GA which returns a reduced set of optimal features. For image classification, an extreme learning machine classifier is incorporated which is much simpler in terms of parameter tuning and learning time as compared to other traditional classifiers. The performance of the proposed method is evaluated on five datasets which highlight the better performance in terms of evaluation metrics as compared with the state-of-the-art image retrieval methods. Its statistical analysis through a nonparametric Wilcoxon matched-pairs signed-rank test also exhibits significant performance.

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Section: Related Workmentioning

confidence: 99%

Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval

et al. 2022

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“…In that article, they compared the results with color histogram, and the proposed approach works fine with the Wang dataset. Mary et al [40] used color moments as a color descriptor in their paper and combined it with gray-level co-occurrence matrices (GLCM) and Fourier descriptor. Effat et al [41] used color moments and blended it with other texture and shape descriptors, but the focus was mainly on machine learning techniques like K-means clustering, and neural network along with genetic algorithm.…”

Section: Traditional Retrieval Practicesmentioning

confidence: 99%

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Chavda

Goyani

2020

SN COMPUT. SCI.

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The objective of the content-based image retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependence of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors: global color histogram and multi-scale local binary pattern (MS-LBP); furthermore, the use of PCA for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets, viz., Corel-1k, Corel-5k, Corel-10k, and Ghim-10k together with result comparison based on the precision and recall values at different thresholds. The classification purposes are satisfied with Euclidean and City Block distance. The performance study of the proposed work displays it as outperformer than the identified state-of-the-art literature.

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Optimized Color Correlogram based CBIR to beat the hole of Information and Interpretation

Soni

Lamba²,

Kumar³

2021

2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT)

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An optimized feature selection CBIR technique using ANN

Cited by 3 publications

References 15 publications

Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval

Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Optimized Color Correlogram based CBIR to beat the hole of Information and Interpretation

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