Classification of digital photos taken by photographers or home users

Hanghang Tong, Mingjing Li, Hong Jiang Zhang, Jingrui He, Changshui Zhang

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

In this paper, we address a specific image classification task, i.e. to group images according to whether they were taken by photographers or home users. Firstly, a set of low-level features explicitly related to such high-level semantic concept are investigated together with a set of general-purpose low-level features. Next, two different schemes are proposed to find out those most discriminative features and feed them to suitable classifiers: one resorts to boosting to perform feature selection and classifier training simultaneously; the other makes use of the information of the label by Principle Component Analysis for feature reextraction and feature de-correlation; followed by Maximum Marginal Diversity for feature selection and Bayesian classifier or Support Vector Machine for classification. In addition, we show an application in No-Reference holistic quality assessment as a natural extension of such image classification. Experimental results demonstrate the effectiveness of our methods.

Original languageEnglish (US)
Pages (from-to)198-205
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3331
StatePublished - 2004
Externally publishedYes

Fingerprint

Image Classification
Feature Selection
Classifiers
Image classification
Classifier
Principle Component Analysis
Bayesian Classifier
Feature extraction
Quality Assessment
Boosting
Natural Extension
Support Vector Machine
Semantics
Support vector machines
Labels
Experimental Results
Demonstrate
Concepts
Training

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

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