Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval.

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ID: 32714
2017
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Ranked #40 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
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 vector quantization -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 is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with 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.
Reference Key
peizhong-liu2017fusionieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Peizhong Liu, ;Jing-Ming Guo, ;Chi-Yi Wu, ;Danlin Cai, ;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2017
DOI
10.1109/TIP.2017.2736343
URL
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