Image based search system using hierarchical object category recognition technique

Takuya Minagawa, Hideo Saito

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

1 Citation (Scopus)

Abstract

In this paper, we present the information search system using object category recognition, which is queried by image from mobile phone camera or from photo sharing service on internet. In such system, processing speed is an important requirement. We adopted "Standard Model" proposed by T. Serre in 2005, and improved processing speed by replacing Gabor filter to Haar wavelet, vector quantization of feature patch, and restriction of calculation area. In addition, by retaining the information of each feature's position, it compensates the accuracy which is a little reduced in exchange of processing speed. We implemented this method to server system, and proved this system can work in practical processing time. Through the experiment for Caltech-101 image database, we confirmed value of this system.

Original languageEnglish
Title of host publicationProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
Pages219-222
Number of pages4
Publication statusPublished - 2009 Dec 1
Event11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, Japan
Duration: 2009 May 202009 May 22

Publication series

NameProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009

Other

Other11th IAPR Conference on Machine Vision Applications, MVA 2009
CountryJapan
CityYokohama
Period09/5/2009/5/22

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Image based search system using hierarchical object category recognition technique'. Together they form a unique fingerprint.

  • Cite this

    Minagawa, T., & Saito, H. (2009). Image based search system using hierarchical object category recognition technique. In Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009 (pp. 219-222). (Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009).