A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Open Systems & Information Dynamics
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Topic-conditioned novelty detection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACM Computing Surveys (CSUR)
Anomaly detection in multidimensional data using negative selection algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Experiments with patch-based object classification
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Patch-based experiments with object classification in video surveillance
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
EvIdentTM: a functional magnetic resonance image analysis system
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
Video retrieval and video mining has become a very popular research field due to the increase of applications like video monitoring, video surveillance, video matching and duplication detection in videos and also due to and hasty raise in the volume of digital video databases. Since it is a vibrant research area, in this research work, an efficient approach to find new objects from video is proposed. This research work is aimed to find out the object from video using Lorentz Information measure and Discrete Wavelet Transform to extract the features of the video frame and applying self organizing maps to recognize new objects in a frame of video sequence. The proposed model of the system is designed and implemented on MATLAB. The proposed system tested with the sample videos and providing promising results. The performance level of the proposed system will be suited for several simple day to day video mining applications and video surveillance systems.