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About Content-Based Image Retrieval Using Deep Learning
This thesis proposes using deep learning techniques for content-based image retrieval. Specifically, it uses a convolutional neural network trained on a dataset of annotated images. The previous approach to CBIR used low-level visual features and bag-of-words models, which suffered from the semantic gap between low-level features and high-level semantics. By using a CNN to learn hierarchical feature representations directly from images, the proposed approach aims to bridge this semantic gap. The thesis describes training a LeNet CNN on the dataset and using the learned features to retrieve similar images based on a query. Evaluation shows the deep learning approach achieves better retrieval performance than the previous bag-of-words method. Future work includes exploring more advanced CNN architectures and larger
Detailed Information
| Author: | ['Thu Trang Nguyễn Thị'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 44 |
| Language: | English |
| Format: | |
| Price: | FREE |
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