Visual reranking with improved image graph

This paper introduces an improved reranking method for the Bag-of-Words (BoW) based image search. Built on [1], a directed image graph robust to outlier distraction is proposed. In our approach, the relevance among images is encoded in the image graph, based on which the initial rank list is refined. Moreover, we show that the rank-level feature fusion can be adopted in this reranking method as well. Taking advantage of the complementary nature of various features, the reranking performance is further enhanced. Particularly, we exploit the reranking method combining the BoW and color information. Experiments on two benchmark datasets demonstrate that our method yields significant improvements and the reranking results are competitive to the state-of-the-art methods.

• Publication year

  2014

• Venue

  IEEE International Conference on Acoustics, Speech, and Signal Processing

• Publication date

  2014-05-04

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/ICASSP.2014.6854935arXiv 1406.0680
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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