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

Abstract

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
Pages114-130
Number of pages17
ISBN (Print)9781466629417, 1466629401, 9781466629400
DOIs
StatePublished - Feb 28 2013

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Zhu, Q., Lin, L., Shyu, M-L., & Liu, D. (2013). Utilizing context information to enhance content-based image classification. In Multimedia Data Engineering Applications and Processing (pp. 114-130). IGI Global. https://doi.org/10.4018/978-1-4666-2940-0.ch006