Improved HOG Descriptors in Image Classification with CP Demonstration

Tan Vo, Dat TRAN, Wanli MA, Khoa NGUYEN

Research output: A Conference proceeding or a Chapter in BookConference 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 languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publicationLecture Notes in Computer Science
EditorsMinho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
Place of PublicationGermany
PublisherSpringer
Pages384-391
Number of pages8
Volume8228
ISBN (Electronic)9783642420511
ISBN (Print)9783642420504
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Conference

Conference20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated titleICONIP 2013
CountryKorea, Republic of
CityDaegu
Period3/11/137/11/13

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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, & 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