Utilizing context information to enhance content-based image classification

Qiusha Zhu, Lin Lin, Mei-Ling Shyu, Dianting Liu

Research output: Chapter in Book/Report/Conference proceedingChapter


Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users' expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.

Original languageEnglish (US)
Title of host publicationMultimedia Data Engineering Applications and Processing
PublisherIGI Global
Number of pages17
ISBN (Print)9781466629417, 1466629401, 9781466629400
StatePublished - Feb 28 2013

ASJC Scopus subject areas

  • Computer Science(all)


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