Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Rotation invariant distance measures for trajectories
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of Artificial Intelligence Research
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Motion-based video retrieval by trajectory matching
IEEE Transactions on Circuits and Systems for Video Technology
Multifeature Object Trajectory Clustering for Video Analysis
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Many feature transforms have been proposed for the problem of trajectory matching. These methods, which are often based on shape matching, tend to perform poorly for biological trajectories, such as cell motion, because similar biological behavior often results in dissimilar trajectory shape. Additionally, the criteria used for similarity may differ depending on the user's particular interest or the specific query behavior. We present a rank-based distance metric learning method that combines user input and a new set of biologically-motivated features for biological trajectory matching. We show that, with a small amount of user effort, this method outperforms existing trajectory methods. On an information retrieval task using real world data, our method outperforms recent, related methods by ~9%.