Boosting object retrieval by estimating pseudo-objects

  • Authors:

  • Kuan-Hung Lin;Kuan-Ting Chen;Winston H. Hsu;Chun-Jen Lee;Tien-Hsu Li

  • Affiliations:

  • National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan;Chunghwa Telecom Co. Ltd., Taipei, Taiwan;Chunghwa Telecom Co. Ltd., Taipei, Taiwan

  • Venue:
  • ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
  • Year:
  • 2009

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Abstract

State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. A search is performed by comparing query object's feature vector with those for database images. However, a database image vector generally carries mixed information of an entire image which may contain multiple objects and background. Search quality is degraded by such noisy (or diluted) feature vectors. We address this issue by introducing the concept of pseudo-objects to approximate candidate objects in database images. A pseudo-object is a subset of proximate feature points in an image with its own feature vector to represent a local area. We investigate effective methods (e.g., Grid, G-means, and GMM-BIC) to estimate pseudo-objects. Experimenting over two consumer photo benchmarks, we demonstrate the proposed methods significantly outperforming other state-of-the-art object retrieval algorithms.