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标题:Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval.
时间:2020-02-14 22:28:02
DOI:10.1109/TIP.2017.2736343
PMID:28866491
作者:Peizhong Liu;Jing-Ming Guo;Chi-Yi Wu
关键词:Content-Based Image Retrieval;Deep Learning;Convolutional-Neural Network;Block Truncation Coding;Halftoning
出版源: 《IEEE Transactions on Image Processing A Public... ,2017 ,PP (99) :1-1
摘要:This paper presents an effective image retrieval method by combining high-level features from Convolutional Neural Network (CNN) model and low-level features from Dot-Diffused Block Truncation Coding (DDBTC). The low-level features, e.g., texture and color, are constructed by VQ-indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features (DL-TLCF) is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate (APR) and average recall rate (ARR), are employed to examine various datasets. As documented in the experimental results, the proposed schemes can achieve superior performance compared to the state-of-the-art methods with either low- or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.
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