Floating search methods in feature selection
Pattern Recognition Letters
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cinematic Primitives for Multimedia
IEEE Computer Graphics and Applications
A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Combined key-frame extraction and object-based video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Evaluation of video summarization for a large number of cameras in ubiquitous home
Proceedings of the 13th annual ACM international conference on Multimedia
Temporal video structuring for preservation and annotation of video content
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Shot retrieval based on fuzzy evolutionary aiNet and hybrid features
Computers in Human Behavior
Multimedia retrieval from a large number of sources in a ubiquitous environment
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
A design-of-experiment based statistical technique for detection of key-frames
Multimedia Tools and Applications
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In this paper, we propose a coherent framework for joint key-frame extraction and object-based video segmentation. Conventional key-frame extraction and object segmentation are usually implemented independently and separately due to the fact that they are on different semantic levels. This ignores the inherent relationship between key-frames and objects. The proposed method extracts a small number of key-frames within a shot so that the divergence between video objects in a feature space can be maximized, supporting robust and efficient object segmentation. This method can utilize advantages of both temporal and object-based video segmentations, and be helpful to build a unified framework for content-based analysis and structured video representation. Theoretical analysis and simulation results on both synthetic and real video sequences manifest the efficiency and robustness of the proposed method.