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A review of object representation based on local features

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Abstract

Object representation based on local features is a topical subject in the domain of image understanding and computer vision. We discuss the defects of global features in present methods and the advantages of local features in object recognition, and briefly explore state-of-the-art recognition methods using local features, especially the main approaches of local feature extraction and object representation. To clearly explain these methods, the problem of local feature extraction is divided into feature region detection, feature region description, and feature space optimization. The main components and merits of these steps are presented. Technologies for object presentation are classified into three types: vector space, sliding window, and structure relationship models. Future development trends are discussed briefly.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China

    Jian Cao, Dian-hui Mao, Qiang Cai & Hai-sheng Li

  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jun-ping Du

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  1. Jian Cao
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  4. Hai-sheng Li
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Correspondence to Jian Cao.

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Project supported by the National Basic Research Program (973) of China (No. 2012CB821206), the National Natural Science Foundation of China (No. 71201004), the Scientific Research Common Program of Beijing Municipal Commission of Education (No. KM201310011009), and the Funding Project for Innovation on Science, Technology and Graduate Education in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (Nos. PXM2012_014213_000037 and PXM2012_014213_000079)

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Cao, J., Mao, Dh., Cai, Q. et al. A review of object representation based on local features. J. Zhejiang Univ. - Sci. C 14, 495–504 (2013). https://doi.org/10.1631/jzus.CIDE1303

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