Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building compact local pairwise codebook with joint feature space clustering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Discriminative spatial pyramid
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Recognizing jumbled images: The role of local and global information in image classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Compact correlation coding for visual object categorization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Modeling spatial layout with fisher vectors for image categorization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Spatial information in images is considered to be of great importance in the process of object recognition. Recent studies show that human's classification accuracy might drop dramatically if the spatial information of an image is removed. The original bag-of-words (BoW) model is actually a system simulating such a classification process with incomplete information. To handle the spatial information, spatial pyramid matching (SPM) was proposed, which has become the most widely used scheme in the purpose of spatial modeling. Given an image, SPM divides it into a series of spatial blocks on several levels and concatenates the representations obtained separately within all the blocks. SPM greatly improves the performance since it embeds spatial information into BoW. However, SPM ignores the relationships between the spatial blocks. To address this problems, we propose a new scheme based on a spatial graph, whose nodes correspond to the spatial blocks in SPM, and edges correspond to the relationships between the blocks. Thorough experiments on several popular datasets verify the advantages of the proposed scheme.