SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Large Scale Online Learning of Image Similarity Through Ranking
The Journal of Machine Learning Research
Action Recognition Using Mined Hierarchical Compound Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
One shot similarity metric learning for action recognition
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Action Similarity Labeling Challenge
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper we present a novel similarity measure between two image sequences, that is a)robust to different viewpoints and recording conditions (illumination variations and clothing) b)robust to geometric transformations (translation, scale and rotation transformations) and c)invariant to the number of frames of the image sequence as well as of its time scaling. More precisely, we create a similarity measure that exploits the underlying relationships among neighborhoods of detected spatiotemporal points in a frame of an image sequence. We find the space in which the similarities of neighboring spatiotemporal points lie in, and map it to another space of smaller dimensionality. In the new space the projected similarities are of fixed dimensionality, depending on the number of neighbors we have considered. We use the information about that newly extracted space to define a novel similarity measure between two image sequences and create in that way a similarity vector that can be used as an input to a classifier. We apply the proposed similarity measure to the 'action pair matching' problem, in which we try to decide whether two action image sequences contain the same action or not. Experiments conducted using the Action Similarity Labeling (ASLAN) dataset verify the superiority of the proposed method over state of the art techniques in terms of accuracy rate.