Query and Feedback Technologies in Multimedia Information Retrieval

Zha Zhengjun; Zheng Xiaoju

doi:10.7544/issn1000-1239.2017.20170004

Journal of Computer Research and Development > 2017 > 54(6): 1267-1280. > DOI: 10.7544/issn1000-1239.2017.20170004 CSTR: 32373.14.issn1000-1239.2017.20170004

Zha Zhengjun, Zheng Xiaoju. Query and Feedback Technologies in Multimedia Information Retrieval[J]. Journal of Computer Research and Development, 2017, 54(6): 1267-1280. DOI: 10.7544/issn1000-1239.2017.20170004

Citation:

PDF (6829 KB)

Query and Feedback Technologies in Multimedia Information Retrieval

Zha Zhengjun¹,
Zheng Xiaoju^1,2

¹(School of Information Science and Technology, University of Science and Technology of China, Hefei 230027)
²(Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031)

More Information

Published Date: May 31, 2017

Graphical Abstract

Abstract

Abstract

In spite of the remarkable progress made in the past decades, multimedia information retrieval still suffers from the “intention gap” and “semantic gap”. To address this issue, researchers have proposed a wealth of query technologies to help user express search intent clearly as well as feedback technologies to help retrieval system understand user intent and multimedia data accurately, leading to significant improvements of retrieval performance. This paper presents a survey of the query and feedback technologies in multimedia information retrieval. We summarize the evolution of query styles and the development of feedback approaches. We elaborate the query approaches for retrieval on PC, mobile intelligent devices and touch-screen devices etc. We introduce the feedback approaches proposed in different periods and discuss the interaction issue in exploratory multimedia retrieval. Finally, we discuss future research directions in this field.
- multimedia information retrieval,
- retrieval intent,
- content understanding,
- query,
- feedback

FullText(HTML)

References (0)

[1]	Zhang Long, Wang Jinsong. DDoS Attack Detection Model Based on Information Entropy and DNN in SDN[J]. Journal of Computer Research and Development, 2019, 56(5): 909-918. DOI: 10.7544/issn1000-1239.2019.20190017
[2]	Zhou Yanhong, Zhang Xianyong, Mo Zhiwen. Conditional Neighborhood Entropy with Granulation Monotonicity and Its Relevant Attribute Reduction[J]. Journal of Computer Research and Development, 2018, 55(11): 2395-2405. DOI: 10.7544/issn1000-1239.2018.20170607
[3]	Li Feng, Miao Duoqian, Zhang Zhifei, Zhang Wei. Mutual Information Based Granular Feature Weighted k-Nearest Neighbors Algorithm for Multi-Label Learning[J]. Journal of Computer Research and Development, 2017, 54(5): 1024-1035. DOI: 10.7544/issn1000-1239.2017.20160351
[4]	Dong Hongbin, Teng Xuyang, Yang Xue. Feature Selection Based on the Measurement of Correlation Information Entropy[J]. Journal of Computer Research and Development, 2016, 53(8): 1684-1695. DOI: 10.7544/issn1000-1239.2016.20160172
[5]	Tang Liangrui, Chen Yuanyuan, and Feng Sen. A Chain Routing Algorithm Based on Evidence Theory in Wireless Sensor Networks[J]. Journal of Computer Research and Development, 2013, 50(7): 1362-1369.
[6]	Xu Junling, Zhou Yuming, Chen Lin, Xu Baowen. An Unsupervised Feature Selection Approach Based on Mutual Information[J]. Journal of Computer Research and Development, 2012, 49(2): 372-382.
[7]	Yang Chunfang, Liu Fenlin, and Luo Xiangyang. Histograms Difference and Quantitative Steganalysis of JPEG Steganography Based on Relative Entropy[J]. Journal of Computer Research and Development, 2011, 48(8): 1563-1569.
[8]	Wang Wenhui, Feng Qianjin, Chen Wufan. Segmentation of Brain MR Images Based on the Measurement of Difference of Mutual Information and Gauss-Markov Random Field Model[J]. Journal of Computer Research and Development, 2009, 46(3): 521-527.
[9]	Xiong Zhongmin, Hao Zhongxiao. An Approach to Termination Decision for a Rule Set Based on Activation Path and Conditional Formula[J]. Journal of Computer Research and Development, 2006, 43(5): 901-907.
[10]	Wang Xizhao and An Sufang. Research on Learning Weights of Fuzzy Production Rules Based on Maximum Fuzzy Entropy[J]. Journal of Computer Research and Development, 2006, 43(4): 673-678.