Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A comparison of color features for visual concept classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
Computer
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
Visual word spatial arrangement for image retrieval and classification
Pattern Recognition
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Currently, bag-of-words approaches for image categorization are very popular due to their relative simplicity, robustness and high efficiency. However, they lack the ability to represent the spatial composition of an image. This drawback has been addressed by several approaches, with spatial pyramids being the most popular. Spatial pyramids divide an image into smaller blocks, resulting in a feature vector for each block of the image. The feature vectors for these blocks are concatenated to form the feature vector of the whole image. This leads to an increase in dimension of the whole image's feature vector by a factor corresponding to the number of blocks the image is divided into. Consequently, this causes an increase in computation time proportional to the number of blocks. We propose an extension of the image feature vector by spatial features, which results in a descriptor of similar size as in the standard bag-of-words approach. The classification performance however is similar to those of spatial pyramids which use a feature vector of significantly larger size and therefore are more computationally expensive.