APSIPA Transactions on Signal and Information Processing > Vol 5 > Issue 1

A survey on compact features for visual content analysis

Luca Baroffio, Informazione e Bioingegneria, Italy, Alessandro E. C. Redondi, Informazione e Bioingegneria, Italy, alessandroenrico.redondi@polimi.it , Marco Tagliasacchi, Informazione e Bioingegneria, Italy, Stefano Tubaro, Informazione e Bioingegneria, Italy
 
Suggested Citation
Luca Baroffio, Alessandro E. C. Redondi, Marco Tagliasacchi and Stefano Tubaro (2016), "A survey on compact features for visual content analysis", APSIPA Transactions on Signal and Information Processing: Vol. 5: No. 1, e13. http://dx.doi.org/10.1017/ATSIP.2016.13

Publication Date: 20 Jun 2016
© 2016 Luca Baroffio, Alessandro E. C. Redondi, Marco Tagliasacchi and Stefano Tubaro
 
Subjects
 
Keywords
Visual featuresKeypointDetectorDescriptorExtractionCompressionNetworkingEncodingSIFTMobile visual searchVisual sensor networks
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. LOCAL FEATURE EXTRACTION 
III. VISUAL FEATURE COMPRESSION 
IV. FEATURES EXTRACTED FROM VIDEO SEQUENCES 
V. VISUAL FEATURE TRANS-MISSION AND NETWORKING 
VI. CONCLUSION 

Abstract

Visual features constitute compact yet effective representations of visual content, and are being exploited in a large number of heterogeneous applications, including augmented reality, image registration, content-based retrieval, and classification. Several visual content analysis applications are distributed over a network and require the transmission of visual data, either in the pixel or in the feature domain, to a central unit that performs the task at hand. Furthermore, large-scale applications need to store a database composed of up to billions of features and perform matching with low latency. In this context, several different implementations of feature extraction algorithms have been proposed over the last few years, with the aim of reducing computational complexity and memory footprint, while maintaining an adequate level of accuracy. Besides extraction, a large body of research addressed the problem of ad-hoc feature encoding methods, and a number of networking and transmission protocols enabling distributed visual content analysis have been proposed. In this survey, we present an overview of state-of-the-art methods for the extraction, encoding, and transmission of compact features for visual content analysis, thoroughly addressing each step of the pipeline and highlighting the peculiarities of the proposed methods.

DOI:10.1017/ATSIP.2016.13