A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel

Hirotaka Niitsuma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper proposes an extension of support vector machines (SVMs) for recognizing position and size of objects in digital images. The discriminant function is given as an analytical function of the object position and size. Using Fisher kernel, a concept of Retinotopical Sampling(RS) is introduced to SVMs.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsV. Palade, R.J. Howlett, L. Jain
Pages155-161
Number of pages7
Volume2774 PART 2
Publication statusPublished - 2003
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: Sep 3 2003Sep 5 2003

Other

Other7th International Conference, KES 2003
CountryUnited Kingdom
CityOxford
Period9/3/039/5/03

Fingerprint

Support vector machines
Sampling

ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Niitsuma, H. (2003). A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel. In V. Palade, R. J. Howlett, & L. Jain (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2774 PART 2, pp. 155-161)

A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel. / Niitsuma, Hirotaka.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / V. Palade; R.J. Howlett; L. Jain. Vol. 2774 PART 2 2003. p. 155-161.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Niitsuma, H 2003, A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel. in V Palade, RJ Howlett & L Jain (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 2774 PART 2, pp. 155-161, 7th International Conference, KES 2003, Oxford, United Kingdom, 9/3/03.
Niitsuma H. A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel. In Palade V, Howlett RJ, Jain L, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 2774 PART 2. 2003. p. 155-161
Niitsuma, Hirotaka. / A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / V. Palade ; R.J. Howlett ; L. Jain. Vol. 2774 PART 2 2003. pp. 155-161
@inproceedings{0d2fdceef4a844c193c9dbab90edc1fe,
title = "A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel",
abstract = "The paper proposes an extension of support vector machines (SVMs) for recognizing position and size of objects in digital images. The discriminant function is given as an analytical function of the object position and size. Using Fisher kernel, a concept of Retinotopical Sampling(RS) is introduced to SVMs.",
author = "Hirotaka Niitsuma",
year = "2003",
language = "English",
volume = "2774 PART 2",
pages = "155--161",
editor = "V. Palade and R.J. Howlett and L. Jain",
booktitle = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

}

TY - GEN

T1 - A trainable Obj ect-detection method using equivalent retinotopical sampling and Fisher Kernel

AU - Niitsuma, Hirotaka

PY - 2003

Y1 - 2003

N2 - The paper proposes an extension of support vector machines (SVMs) for recognizing position and size of objects in digital images. The discriminant function is given as an analytical function of the object position and size. Using Fisher kernel, a concept of Retinotopical Sampling(RS) is introduced to SVMs.

AB - The paper proposes an extension of support vector machines (SVMs) for recognizing position and size of objects in digital images. The discriminant function is given as an analytical function of the object position and size. Using Fisher kernel, a concept of Retinotopical Sampling(RS) is introduced to SVMs.

UR - http://www.scopus.com/inward/record.url?scp=8344276700&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=8344276700&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:8344276700

VL - 2774 PART 2

SP - 155

EP - 161

BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

A2 - Palade, V.

A2 - Howlett, R.J.

A2 - Jain, L.

ER -