Making image to class distance comparable

Deyuan Zhang; Bingquan Liu; Chengjie Sun; Xiaolong Wang

doi:10.1007/978-3-642-24958-7_78

Making image to class distance comparable

Deyuan Zhang^*
, Bingquan Liu
, Chengjie Sun
, Xiaolong Wang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Image classification is to classify the image into predefined image categories. The image to class distance(I2CD), with simple formulation, can tackle the intra-class variation and show the state of the art results in several datasets. This paper focuses on the performance of I2CD on imbalanced training dataset which has not been catched much attention by I2CD researchers. Under the naive bayes assumption, when the dataset is imbalanced, I2CD is not comparable. We propose Random Sampling I2CD to tackle the imbalanced problem, and provide an efficient approximation method to reduce the test time complexity. Experimental results show that PRSI2CD outperforms the original I2CD in imbalanced setting.

Original language	English
Title of host publication	Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages	671-680
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-24958-7_78
State	Published - 2011
Externally published	Yes
Event	18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7063 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Neural Information Processing, ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11

Keywords

image classification
image to class distance
imbalanced dataset

Access to Document

10.1007/978-3-642-24958-7_78

Cite this

Zhang, D., Liu, B., Sun, C., & Wang, X. (2011). Making image to class distance comparable. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings (PART 2 ed., pp. 671-680). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7063 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-24958-7_78

@inproceedings{2877ca6e3bc54a758312404f79c8c130,

title = "Making image to class distance comparable",

abstract = "Image classification is to classify the image into predefined image categories. The image to class distance(I2CD), with simple formulation, can tackle the intra-class variation and show the state of the art results in several datasets. This paper focuses on the performance of I2CD on imbalanced training dataset which has not been catched much attention by I2CD researchers. Under the naive bayes assumption, when the dataset is imbalanced, I2CD is not comparable. We propose Random Sampling I2CD to tackle the imbalanced problem, and provide an efficient approximation method to reduce the test time complexity. Experimental results show that PRSI2CD outperforms the original I2CD in imbalanced setting.",

keywords = "image classification, image to class distance, imbalanced dataset",

author = "Deyuan Zhang and Bingquan Liu and Chengjie Sun and Xiaolong Wang",

year = "2011",

doi = "10.1007/978-3-642-24958-7\_78",

language = "英语",

isbn = "9783642249570",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "671--680",

booktitle = "Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings",

edition = "PART 2",

note = "18th International Conference on Neural Information Processing, ICONIP 2011 ; Conference date: 13-11-2011 Through 17-11-2011",

}

Zhang, D, Liu, B , Sun, C & Wang, X 2011, Making image to class distance comparable. in Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7063 LNCS, pp. 671-680, 18th International Conference on Neural Information Processing, ICONIP 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24958-7_78

Making image to class distance comparable. / Zhang, Deyuan; Liu, Bingquan ; Sun, Chengjie et al.
Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 2. ed. 2011. p. 671-680 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7063 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Making image to class distance comparable

AU - Zhang, Deyuan

AU - Liu, Bingquan

AU - Sun, Chengjie

AU - Wang, Xiaolong

PY - 2011

Y1 - 2011

N2 - Image classification is to classify the image into predefined image categories. The image to class distance(I2CD), with simple formulation, can tackle the intra-class variation and show the state of the art results in several datasets. This paper focuses on the performance of I2CD on imbalanced training dataset which has not been catched much attention by I2CD researchers. Under the naive bayes assumption, when the dataset is imbalanced, I2CD is not comparable. We propose Random Sampling I2CD to tackle the imbalanced problem, and provide an efficient approximation method to reduce the test time complexity. Experimental results show that PRSI2CD outperforms the original I2CD in imbalanced setting.

AB - Image classification is to classify the image into predefined image categories. The image to class distance(I2CD), with simple formulation, can tackle the intra-class variation and show the state of the art results in several datasets. This paper focuses on the performance of I2CD on imbalanced training dataset which has not been catched much attention by I2CD researchers. Under the naive bayes assumption, when the dataset is imbalanced, I2CD is not comparable. We propose Random Sampling I2CD to tackle the imbalanced problem, and provide an efficient approximation method to reduce the test time complexity. Experimental results show that PRSI2CD outperforms the original I2CD in imbalanced setting.

KW - image classification

KW - image to class distance

KW - imbalanced dataset

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Zhang D, Liu B , Sun C, Wang X. Making image to class distance comparable. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 2 ed. 2011. p. 671-680. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-24958-7_78

Making image to class distance comparable

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