Multiple-window Bag of Features for Road Environment Recognition

The idea of Bag of Features (BoF) is recently often employed for general object recognition. But, as it does not take positional relations of detected features into account, the recognition rate is still not very high for practical use. This paper proposes a method of describing the feature of an object by the BoF representation which considers positional information of the features. Although the original BoF representation is applied to an entire image, the proposed method employs multiple windows on an image. The BoF representation is applied to each of the windows to represent an object in the image interested for recognition. The performance of the proposed method is shown experimentally.

[1]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

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

[3]  James F. Korsh Data structures, algorithms, and program style , 1986 .

[4]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[6]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[7]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).