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篇名 |
Prompt Image Search with Deep Convolutional Neural Network via Efficient Hashing Code and Addictive Latent Semantic Layer
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並列篇名 | Prompt Image Search with Deep Convolutional Neural Network via Efficient Hashing Code and Addictive Latent Semantic Layer |
作者 | Jun-yi Li、Jian-hua Li |
英文摘要 | As we know that the nearest neighbor search is a good and effective method for good-sized image search. This paper indicates a vision learning framework to generate compact binary hash codes for quick vision search after knowing the recent benefits of convolution neural networks (CNN). Our concept is that binary codes can be obtained using a hidden layer to present some latent concepts dominating the class labels with usable data labels. CNN also can be used to learn image representations. Binary code learning is required for other supervised methods. However, our method is effective in obtaining hash codes and image representations and we use pretrained model from googlenet for incremental learning so it is suitable for good-sized dataset. It is demonstrated in our experiment that this method is better than some most advanced hashing algorithms in MINIST, NUSWIDE and CIFAR-10 dataset. The scalability and efficiency still needs to be further investigated in a good-sized dataset. |
起訖頁 | 947-955 |
關鍵詞 | Convolutional neural networks、Nearest neighbor search、hidden layer、LSH、Supervised learning |
刊名 | 網際網路技術學刊 |
期數 | 201805 (19:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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