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Content-based image retrieval using Gaussian-Hermite moments and firefly and grey wolf optimization 被引量:1
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作者 yasasvy tadepalli Meenakshi Kollati +3 位作者 Swaraja Kuraparthi Padmavathi Kora Anil Kumar Budati Lakshmi Kala Pampana 《CAAI Transactions on Intelligence Technology》 EI 2021年第2期135-146,共12页
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 Gaus... 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 accuracyof the classifier.The FAGWO algorithm derives the optimum weights from a randomlygenerated 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. 展开更多
关键词 PYTHON OPTIMIZATION IMAGE
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