Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale robust visual codebook construction
Proceedings of the international conference on Multimedia
Bridging the Semantic Gap Between Image Contents and Tags
IEEE Transactions on Multimedia
Self-paced dictionary learning for image classification
Proceedings of the 20th ACM international conference on Multimedia
Multimodal late fusion bag of features applied to scene detection
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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The use of bag-of-features (BOF) models has been a popular technique for image classification and retrieval. In order to better represent and discriminate images from different classes, we advance BOF and explore the self-similarities of visual words for improved performance. The proposed self-similarity hypercubes (SSH) model, which observes the concurrent occurrences of visual words in an image, is able to describe the structural information of the BOF in an image. Our experiments confirm that our SSH provides additional and complementary information to BOF and thus results in improved classification performance. Unlike most prior methods requiring extraction or integration of multiple types of features for similar improvements, our SSH works in the same domain as the BOF does. Moreover, we do not limit the use of our SSH to any particular type of image descriptors, and its generalization is also verified.