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- W4285245085 abstract "Feature extraction, representation, and similarity estimation are all essential to measure the performance of a content-based image retrieval (CBIR) system, and they have all been widely studied for decades. Although numerous solutions have been projected, the semantic gap remains one of the most challenging problems in the ongoing research of CBIR. The semantic gap talks about how pixels in an image are perceived by computers and how humans perceive images. In recent years, machine learning and deep learning approaches have made considerable progress in addressing this issue. Proposed research work uses deep architectures to model high-level abstractions in data. Deep learning is modeled as an intelligent architecture that integrates data and information through various transformations and representations. Deep learning techniques enable a computer to learn many complicated functions that link preprocessed input data to output data without domain knowledge or human-crafted features. We have used a multi-class weather dataset and Wang’s dataset to measure the effectiveness of retrieval efficiency. The AlexNet and VGG16 are used for training and testing. Developed systems are tested with a testing dataset, and the results are compared with state-of-the-art technology. The VGG16 outperformed category-wise and also concerning mean average precision." @default.
- W4285245085 created "2022-07-14" @default.
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- W4285245085 date "2022-01-01" @default.
- W4285245085 modified "2023-09-29" @default.
- W4285245085 title "Artificial Intelligence Framework for Content-Based Image Retrieval: Performance Analysis" @default.
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- W4285245085 doi "https://doi.org/10.1007/978-981-16-9113-3_39" @default.
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