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 journalArticlepeer-review


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)
StatePublished - 2004
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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