Salient Region Filtering for Background Subtraction

The use of salient regions is an increasingly popular approach to image retrieval. For situations where object retrieval is required and where the foreground and background can be assumed to have different characteristics, it becomes useful to exclude salient regions which are characteristic of the background if they can be identified before matching is undertaken. This paper proposes a technique to enhance the performance of object retrieval by filtering out salient regions believed to be associated with the background area of the images. Salient regions from background only images are extracted and clustered using descriptors representing the salient regions. The clusters are then used in the retrieval process to identify salient regions likely to be part of the background in images containing object and background. Salient regions close to background clusters are pruned before matching and only the remaining salient regions are used in the retrieval. Experiments on object retrieval show that the use of salient region background filtering gives an improvement in performance when compared with the unfiltered method.

[1]  Wei-Ying Ma,et al.  Image and Video Retrieval , 2003, Lecture Notes in Computer Science.

[2]  Nicu Sebe,et al.  Image retrieval using wavelet-based salient points , 2001, J. Electronic Imaging.

[3]  Nicu Sebe,et al.  Evaluation of Salient Point Techniques , 2002, CIVR.

[4]  Jonathon S. Hare,et al.  Salient Regions for Query by Image Content , 2004, CIVR.

[5]  Pietro Perona,et al.  Evaluation of Features Detectors and Descriptors based on 3D Objects , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[6]  Horst Bischof,et al.  A novel performance evaluation method of local detectors on non-planar scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[7]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[8]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[9]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Ling Shao,et al.  Specific object retrieval based on salient regions , 2006, Pattern Recognit..

[12]  Hui Zhang,et al.  Local image representations using pruned salient points with applications to CBIR , 2006, MM '06.