Visual pattern discovery using web images

ABSTRACT

In this paper, a novel approach for discovering visual patterns associated with semantic concepts using web image resources is proposed.This approach can be used to improve the performance in image clustering and retrieval, image annotation, and other applications such as object recognition. Exploring the rich information in web images that represent semantic concepts as both visual content and text information, this research attempts to effectively learn intrinsic patterns related to semantic concepts. Because the quality of learning algorithms is strongly related to the selection of positive and negative samples, positive samples are first selected effectively, then negative samples are determined reliably based on the selected positive samples. Finally, a good quality visual model associated with a semantic concept is built through an unsupervised learning process. The proposed scheme is completely automatic,needing no human intervention,and is robust and reliable for generic images. Experimental results demonstrate the effectiveness of the proposed approach.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

General Terms:
Algorithms

Keywords:
image clustering and retrieval, unsupervised learning, visual pattern discovering, web image mining