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An unsupervised method for clustering images based on their salient regions of interest

Published: 23 October 2006 Publication History

Abstract

We have developed a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. In the implemented model cluster membership is assigned based on feature vectors extracted from salient ROIs. This paper focuses on the experimental evaluation of the proposed approach for several combinations of feature extraction techniques and unsupervised clustering algorithms. The results reported here show that this is a valid approach and encourage further research.

References

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O. Marques, L. M. Mayron, G. B. Borba, and H. R. Gamba. An attention-driven model for grouping similar images with image retrieval applications. Eurasip Journal on Applied Signal Processing (submitted).
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Cited By

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  • (2014)A machine learning based intelligent vision system for autonomous object detection and recognitionApplied Intelligence10.1007/s10489-013-0461-540:2(358-375)Online publication date: 1-Mar-2014
  • (2011)A cognitive approach for robots' vision using unsupervised learning and visual saliencyProceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I10.5555/2023252.2023265(81-88)Online publication date: 8-Jun-2011
  • (2011)A Cognitive Approach for Robots’ Vision Using Unsupervised Learning and Visual SaliencyAdvances in Computational Intelligence10.1007/978-3-642-21501-8_11(81-88)Online publication date: 2011
  • Show More Cited By

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  1. An unsupervised method for clustering images based on their salient regions of interest

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      cover image ACM Conferences
      MM '06: Proceedings of the 14th ACM international conference on Multimedia
      October 2006
      1072 pages
      ISBN:1595934472
      DOI:10.1145/1180639
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 23 October 2006

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      Author Tags

      1. clustering
      2. image retrieval
      3. visual attention

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      MM06
      MM06: The 14th ACM International Conference on Multimedia 2006
      October 23 - 27, 2006
      CA, Santa Barbara, USA

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      Cited By

      View all
      • (2014)A machine learning based intelligent vision system for autonomous object detection and recognitionApplied Intelligence10.1007/s10489-013-0461-540:2(358-375)Online publication date: 1-Mar-2014
      • (2011)A cognitive approach for robots' vision using unsupervised learning and visual saliencyProceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I10.5555/2023252.2023265(81-88)Online publication date: 8-Jun-2011
      • (2011)A Cognitive Approach for Robots’ Vision Using Unsupervised Learning and Visual SaliencyAdvances in Computational Intelligence10.1007/978-3-642-21501-8_11(81-88)Online publication date: 2011
      • (2006)A Forward-Looking User Interface for CBIR and CFIR SystemsProceedings of the Eighth IEEE International Symposium on Multimedia10.1109/ISM.2006.2(779-780)Online publication date: 11-Dec-2006

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