Image classification technology based on mining of frequent item sets

Qing Nie*, Shou Yi Zhan, Jing Xia Su

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a novel method to detect frequent and distinctive feature configuration on a class instance. Each neighborhood of a local feature is described by a list of nonzero indices, and generates a transaction. An efficient mining of frequent item sets algorithm is used to automatically find spatial configurations of local features occurring frequently on a class instance. These mined spatial configurations can be used as special words, incorporate into bag of features classification model. Through evaluation on PASCAL 2007 Visual Recognition Challenge dataset set, the test results show that this mining algorithm is computationally efficient and allows to process large training sets rapidly. Moreover, the mined feature configurations have higher discriminative power compare to individual features.

Original languageEnglish
Title of host publicationProceedings of the 2008 Chinese Conference on Pattern Recognition, CCPR 2008
Pages144-148
Number of pages5
DOIs
Publication statusPublished - 2008
Event2008 Chinese Conference on Pattern Recognition, CCPR 2008 - Beijing, China
Duration: 22 Oct 200824 Oct 2008

Publication series

NameProceedings of the 2008 Chinese Conference on Pattern Recognition, CCPR 2008

Conference

Conference2008 Chinese Conference on Pattern Recognition, CCPR 2008
Country/TerritoryChina
CityBeijing
Period22/10/0824/10/08

Keywords

  • Frequent item sets
  • Image classification
  • Image recognition
  • Object recognition

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