Saliency moments for image categorization

  • Authors:
  • Miriam Redi;Bernard Merialdo

  • Affiliations:
  • EURECOM, Sophia Antipolis, route des crêtes, Sophia-Antipolis;EURECOM, Sophia Antipolis, route des crêtes, Sophia-Antipolis

  • Venue:
  • Proceedings of the 1st ACM International Conference on Multimedia Retrieval
  • Year:
  • 2011

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Abstract

In this paper we present Saliency Moments, a new, holistic descriptor for image recognition inspired by two biological vision principles: the gist perception and the selective visual attention. While traditional image features extract either local or global discriminative properties from the visual content, we use a hybrid approach that exploits some coarsely localized information, i.e. the salient regions shape and contours, to build a global, low-dimensional image signature. Results show that this new type of image description outperforms the traditional global features on scene and object categorization, for a variety of challenging datasets. Moreover, we show that, when combined with other existing descriptors (SIFT, Color Moments, Wavelet Feature and Edge Histogram), the saliency-based features provide complementary information, improving the precision of a retrieval system we build for the TRECVID 2010.