FEATURE EXTRACTION FOR LOCALIZED CBIR - What You Click is What you Get

Steven Verstockt, Peter Lambert, Rik Van De Walle

2009

Abstract

This paper addresses the problem of localized content based image retrieval. Contrary to classic CBIR systems which rely upon a global view of the image, localized CBIR only focuses on the portion of the image where the user is interested in, i.e. the relevant content. Using the proposed algorithm, it is possible to recognize an object by clicking on it. The algorithm starts with an automatic gamma correction and bilateral filtering. These pre-processing steps simplify the image segmentation. The segmentation itself uses dynamic region growing, starting from the click position. Contrary to the majority of segmentation techniques, region growing only focuses on that part of the image that contains the object. The remainder of the image is not investigated. This simplifies the recognition process, speeds up the segmentation, and increases the quality of the outcome. Following the region growing, the algorithm starts the recognition process, i.e., feature extraction and matching. Based on our requirements and the reported robustness in many state-of-the-art papers, the Scale Invariant Feature Transform (SIFT) approach is used. Extensive experimentation of our algorithm on three different datasets achieved a retrieval efficiency of approximately 80%.

References

  1. Abdel-Hakim, A.E., Farag, A.A., 2006. CSIFT: A SIFT Descriptor with Color Invariant Characteristics. In CVPR, volume 2, pages 1978-1983.
  2. Bay, H., Tuytelaars, T., Van Gool, L., 2006. SURF: Speeded Up Robust Feature. In ECCV, pages 404-417.
  3. Del Bimbo, A., 1999. Visual Information Retrieval, Morgan Kaufman. San Francisco, 1st edition.
  4. Lowe, D.G., 2004. Distinctive image features from scaleinvariant keypoints. IJCV, 60(2):91-110.
  5. Mikolajczyk, K., Schmid, C., 2004. Comparison of affineinvariant local detectors and descriptors. In EUSIPCO, pages 69-81.
  6. Müller, H., Müller, W., David McG. Squire, MarchandMaillet, S., Pun, T., 2001. Performance evaluation in Content-Based Image retrieval: Overview and Proposals. Pattern recognition letters, 22(5):593-601.
  7. Paris, S., Durand, F., 2006. A fast approximation of the bilateral filter using a signal processing approach. In ECCV, pages 568-580.
  8. Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R., 2000. Content-Based Image Retrieval at the End of the Early Years. TPAMI, 22(12):1349-1380.
  9. Tomasi, C., Manduchi, R., 1998. Bilateral filtering for gray and color images. In ICCV, pages 839-846.
  10. Veltkamp, R.C., Burkhardt, H., Kriegel, H.P., 2001. Stateof-the-Art in Content-Based Image and Video Retrieval, Kluwer AP. Dordrecht, 1st edition.
Download


Paper Citation


in Harvard Style

Verstockt S., Lambert P. and Van De Walle R. (2009). FEATURE EXTRACTION FOR LOCALIZED CBIR - What You Click is What you Get . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 371-374. DOI: 10.5220/0001755703710374


in Bibtex Style

@conference{visapp09,
author={Steven Verstockt and Peter Lambert and Rik Van De Walle},
title={FEATURE EXTRACTION FOR LOCALIZED CBIR - What You Click is What you Get},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={371-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001755703710374},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - FEATURE EXTRACTION FOR LOCALIZED CBIR - What You Click is What you Get
SN - 978-989-8111-69-2
AU - Verstockt S.
AU - Lambert P.
AU - Van De Walle R.
PY - 2009
SP - 371
EP - 374
DO - 10.5220/0001755703710374