Subspace based object recognition using support vector machines

O. G. Sezer; A. Ercil; M. Keskinoz

Subspace based object recognition using support vector machines

O. G. Sezer^*
, A. Ercil
, M. Keskinoz

^*Corresponding author for this work

Sabanci University

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

4 Citations (Scopus)

Abstract

In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects. Comparative analysis of independent component analysis and principal component analysis (PCA) is also given for each experiment.

Original language	English
Title of host publication	13th European Signal Processing Conference, EUSIPCO 2005
Pages	393-396
Number of pages	4
Publication status	Published - 2005
Externally published	Yes
Event	13th European Signal Processing Conference, EUSIPCO 2005 - Antalya, Turkey Duration: 4 Sept 2005 → 8 Sept 2005

Publication series

Name	13th European Signal Processing Conference, EUSIPCO 2005

Conference

Conference	13th European Signal Processing Conference, EUSIPCO 2005
Country/Territory	Turkey
City	Antalya
Period	4/09/05 → 8/09/05

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Cite this

@inproceedings{d7264b97aabc41d2bd59af8b8ce131cf,

title = "Subspace based object recognition using support vector machines",

abstract = "In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects. Comparative analysis of independent component analysis and principal component analysis (PCA) is also given for each experiment.",

author = "Sezer, \{O. G.\} and A. Ercil and M. Keskinoz",

year = "2005",

language = "English",

isbn = "1604238216",

series = "13th European Signal Processing Conference, EUSIPCO 2005",

pages = "393--396",

booktitle = "13th European Signal Processing Conference, EUSIPCO 2005",

note = "13th European Signal Processing Conference, EUSIPCO 2005 ; Conference date: 04-09-2005 Through 08-09-2005",

}

TY - GEN

T1 - Subspace based object recognition using support vector machines

AU - Sezer, O. G.

AU - Ercil, A.

AU - Keskinoz, M.

PY - 2005

Y1 - 2005

N2 - In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects. Comparative analysis of independent component analysis and principal component analysis (PCA) is also given for each experiment.

AB - In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects. Comparative analysis of independent component analysis and principal component analysis (PCA) is also given for each experiment.

UR - https://www.scopus.com/pages/publications/84863637955

M3 - Conference contribution

AN - SCOPUS:84863637955

SN - 1604238216

SN - 9781604238211

T3 - 13th European Signal Processing Conference, EUSIPCO 2005

SP - 393

EP - 396

BT - 13th European Signal Processing Conference, EUSIPCO 2005

T2 - 13th European Signal Processing Conference, EUSIPCO 2005

Y2 - 4 September 2005 through 8 September 2005

ER -

Subspace based object recognition using support vector machines

Abstract

Publication series

Conference

UN SDGs

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