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Random Forest for Image Annotation

  • Hao Fu
  • Qian Zhang
  • Guoping Qiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

In this paper, we present a novel method for image annotation and made three contributions. Firstly, we propose to use the tags contained in the training images as the supervising information to guide the generation of random trees, thus enabling the retrieved nearest neighbor images not only visually alike but also semantically related. Secondly, different from conventional decision tree methods, which fuse the information contained at each leaf node individually, our method treats the random forest as a whole, and introduces the new concepts of semantic nearest neighbors (SNN) and semantic similarity measure (SSM). Thirdly, we annotate an image from the tags of its SNN based on SSM and have developed a novel learning to rank algorithm to systematically assign the optimal tags to the image. The new technique is intrinsically scalable and we will present experimental results to demonstrate that it is competitive to state of the art methods.

Keywords

Random Forest Image Annotation Semantic Nearest Neighbor 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hao Fu
    • 1
  • Qian Zhang
    • 1
  • Guoping Qiu
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK

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