Content-based image retrieval using Gaussian–Hermite moments and firefly and grey wolf optimization
CAAI Transactions on Intelligence Technology, ISSN: 2468-2322, Vol: 6, Issue: 2, Page: 135-146
2021
- 13Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Rapid growth in the transfer of multimedia information over the Internet requires algorithms to retrieve a queried image from large image database repositories. The proposed content-based image retrieval (CBIR) uses Gaussian–Hermite moments as the low-level features. Later these features are compressed with principal component analysis. The compressed feature set is multiplied with the weight matrix array, which has the same size as the feature vector. Hybrid firefly and grey wolf optimization (FAGWO) is used to prevent the premature convergence of optimization in the firefly algorithm. The retrieval of images in CBIR is carried out in an OpenCV python environment with K-nearest neighbours and random forest algorithm classifiers. The fitness function for FAGWO is the accuracy of the classifier. The FAGWO algorithm derives the optimum weights from a randomly generated initial population. When these optimized weights are applied, the proposed algorithm shows better precision/recall and efficiency than other techniques such as exact legendre moments, Region-based image retrieval, K-means clustering and Color descriptor wavelet-based texture descriptor retrieval technique. In terms of optimization, hybrid FAGWO outperformed various optimization techniques (when used alone) like Particle Swarm Optmization, Genetic Algorithm, Grey-Wolf Optimization and FireFly algorithm.
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