Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding   From Videos

doi:10.1109/TIP.2017.2745203

CORC > 北京大学 > 信息科学技术学院

	Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos
	Ding, Lin ; Tian, Yonghong ; Fan, Hongfei ; Wang, Yaowei ; Huang, Tiejun
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2017
关键词	Deep feature convolutional neural networks deep feature coding rate-performance-loss optimization MOBILE VISUAL-SEARCH STANDARD COMPRESSION
DOI	10.1109/TIP.2017.2745203
英文摘要	With the explosion in the use of cameras in mobile phones or video surveillance systems, it is impossible to transmit a large amount of videos captured from a wide area into a cloud for big data analysis and retrieval. Instead, a feasible solution is to extract and compress features from videos and then transmit the compact features to the cloud. Meanwhile, many recent studies also indicate that the features extracted from the deep convolutional neural networks will lead to high performance for various analysis and recognition tasks. However, how to compress video deep features meanwhile maintaining the analysis or retrieval performance still remains open. To address this problem, we propose a high-efficiency deep feature coding (DFC) framework in this paper. In the DFC framework, we define three types of features in a group-of-features (GOFs) according to their coding modes (i.e., I-feature, P-feature, and S-feature). We then design two prediction structures for these features in a GOF, including a sequential prediction structure and an adaptive prediction structure. Similar to video coding, it is important for P-feature residual coding optimization to make a tradeoff between feature bitrate and analysis/retrieval performance when encoding residuals. To do so, we propose a rate-performance-loss optimization model. To evaluate various feature coding methods for large-scale video retrieval, we construct a video feature coding data set, called VFC-1M, which consists of uncompressed videos from different scenarios captured from real-world surveillance cameras, with totally 1M visual objects. Extensive experiments show that the proposed DFC can significantly reduce the bitrate of deep features in the videos while maintaining the retrieval accuracy.; National Basic Research Program of China [2015CB351806]; National Natural Science Foundation of China [U1611461, 61390515, 61425025]; SCI(E); ARTICLE; 12; 5743-5757; 26
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470356]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Ding, Lin,Tian, Yonghong,Fan, Hongfei,et al. Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017.
APA	Ding, Lin,Tian, Yonghong,Fan, Hongfei,Wang, Yaowei,&Huang, Tiejun.(2017).Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos.IEEE TRANSACTIONS ON IMAGE PROCESSING.
MLA	Ding, Lin,et al."Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos".IEEE TRANSACTIONS ON IMAGE PROCESSING (2017).