Improved HOG Descriptors in Image Classification with CP Demonstration

Tan Vo; Dat TRAN; Wanli MA; Khoa NGUYEN

doi:10.1007/978-3-642-42051-1_48

Improved HOG Descriptors in Image Classification with CP Demonstration

Tan Vo, Dat TRAN, Wanli MA, Khoa NGUYEN

Information, Technology (IT) & Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

2 Citations (Scopus)

Abstract

Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.

Original language	English
Title of host publication	International Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publication	Lecture Notes in Computer Science
Editors	Minho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
Place of Publication	Germany
Publisher	Springer
Pages	384-391
Number of pages	8
Volume	8228
ISBN (Electronic)	9783642420511
ISBN (Print)	9783642420504
DOIs	https://doi.org/10.1007/978-3-642-42051-1_48
Publication status	Published - 2013
Event	20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of Duration: 3 Nov 2013 → 7 Nov 2013

Conference

Conference	20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated title	ICONIP 2013
Country	Korea, Republic of
City	Daegu
Period	3/11/13 → 7/11/13

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Cite this

Vo, T., TRAN, D., MA, W., & NGUYEN, K. (2013). Improved HOG Descriptors in Image Classification with CP Demonstration. In M. Lee, A. Hirose, Z-G. Hou, & R. M. Kil (Eds.), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science (Vol. 8228, pp. 384-391). Germany: Springer. https://doi.org/10.1007/978-3-642-42051-1_48

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title = "Improved HOG Descriptors in Image Classification with CP Demonstration",

abstract = "Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.",

keywords = "Age and Gender Classification, Tensor Decomposition, histogram of oriented gradients",

author = "Tan Vo and Dat TRAN and Wanli MA and Khoa NGUYEN",

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language = "English",

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volume = "8228",

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editor = "Minho Lee and Akira Hirose and Zeng-Guang Hou and Kil, {Rhee Man}",

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Vo, T, TRAN, D , MA, W & NGUYEN, K 2013, Improved HOG Descriptors in Image Classification with CP Demonstration. in M Lee, A Hirose, Z-G Hou & RM Kil (eds), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. vol. 8228, Springer, Germany, pp. 384-391, 20th International Conference on Neural Information Processing (ICONIP 2013), Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42051-1_48

Improved HOG Descriptors in Image Classification with CP Demonstration. / Vo, Tan; TRAN, Dat ; MA, Wanli; NGUYEN, Khoa.

International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. ed. / Minho Lee; Akira Hirose; Zeng-Guang Hou; Rhee Man Kil. Vol. 8228 Germany : Springer, 2013. p. 384-391.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution

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N2 - Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.

AB - Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.

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BT - International Conference on Neural Information Processing (ICONIP 2013)

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10.1007/978-3-642-42051-1_48

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