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

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 -in...

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Veröffentlicht in:	IEEE transactions on image processing 2017-12, Vol.26 (12), p.5706-5717
Hauptverfasser:	Liu, Peizhong, Guo, Jing-Ming, Wu, Chi-Yi, Cai, Danlin
Format:	Artikel
Sprache:	eng
Schlagworte:	block truncation coding Computational complexity Content-based image retrieval convolutional-neural network deep learning Feature extraction halftoning Histograms Image coding Image color analysis Image retrieval Machine learning
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