Multilayer feedforward networks are universal approximators
Neural Networks
Video parsing, retrieval and browsing: an integrated and content-based solution
Proceedings of the third ACM international conference on Multimedia
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Time Series Prediction and Neural Networks
Journal of Intelligent and Robotic Systems
Trajectory Segmentation Using Dynamic Programming
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
A hybrid system for affine-invariant trajectory retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Neural networks for event extraction from time series: a back propagation algorithm approach
Future Generation Computer Systems
Global distance-based segmentation of trajectories
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A spectral clustering approach to motion segmentation based on motion trajectory
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Semantic video database system with semi-automatic secondary-content generation capability
Multimedia Tools and Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Image Processing
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With the wide use of monitoring systems, there emerges an ever increasing amount of surveillance videos. Sequential browsing of such videos from the database is time consuming and tedious for users, and thus cannot take full advantage of the rich information contained in video data. In this paper, a general framework for semantic video mining and retrieval is proposed. The framework detects and retrieves semantic events from surveillance videos. It starts by tracking and modeling the trajectories of semantic objects in videos. After that, some general user-interested semantic events are modeled. The goal is to retrieve these semantic events by analyzing the spatiotemporal trajectory sequences. However, since individual users may have their own subjective query targets, these event models may be too general to capture the subjectivity of each individual user. Therefore, in this paper, the mining and retrieval phase is designed to dynamically learn the user's interest by interacting with the user. This technique is called the Relevance Feedback (RF) which is commonly used for Content-based Image Retrieval, but seldom applied to the field of semantic video mining. Due to the spatiotemporal nature of video events, substantial extensions to RF, especially its associated learning mechanisms, are needed to apply it to semantic video mining. The learning framework proposed in this paper bases its structure on the neural network for time series data, which is usually adopted for prediction purposes, and we tailor it to suit the specific needs of spatiotemporal video event mining. In this paper, transportation surveillance videos are used to demonstrate the design details.