Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
On Approximate Nearest Neighbors in Non-Euclidean Spaces
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
On Fuzzy Clustering and Content Based Access to Networked Video Databases
RIDE '98 Proceedings of the Workshop on Research Issues in Database Engineering
A histogram-based moment-preserving clustering algorithm for video segmentation
Pattern Recognition Letters
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Systematic evaluation of logical story unit segmentation
IEEE Transactions on Multimedia
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Video event segmentation and visualisation in non-linear subspace
Pattern Recognition Letters
Relating "Pace' to Activity Changes in Mono- and Multi-camera Surveillance Videos
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
2D-LPI: Two-Dimensional Locality Preserving Indexing
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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In this paper we introduce an effective and unified approach to creating quality video abstractions. The research was motivated by a recently developed subspace learning method called 2D-LPP, or two-dimensional Locality Preserving Projection, which proved to be effective for dimensionality reduction and discriminating enough in 'appearance-based' image recognitions. By exploiting temporal constraints (sequential correlations / contextual content) inherent in a video (vs. random collection of static images) and the use of two 2D-LPP in tandem, an image in the original extremely high (m×n)-dimensional pixel-based image space Im×n is transformed into a point in the compact (d×d)-dimensional feature subspace fd×d, with (d ≪ m) and (d ). This feature subspace has the desired property that visually similar images in Im×n stay close in fd×d and the intrinsic geometry and local structure of the original data are preserved. The feature subspace then lends itself easily to a conventional data clustering technique to identify suitably scattered but temporally connected clusters. If necessary, a global visual colour descriptor can also be used, so the distance metric in clustering incorporates both global and local characteristics. From the clusters, which satisfy some cluster-validity constraints and user requirements (e.g., the number of clusters, most stable or most dynamic content, etc), a summary storyboard of the video is created, comprising pertinent video frames whose features are closest to the centroid of each cluster, for content browsing and search purposes. Experiments on various videos show that the summarisation results are very encouraging when compared with manually acquired 'ground truth'.