Fundamentals of digital image processing
Fundamentals of digital image processing
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
AnchorClu: An Anchorperson Shot Detection Method Based on Clustering
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
An image retrieval system using FPGAs
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abrupt shot change detection using an unsupervised clustering of multiple features
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Acceleration of a content-based image-retrieval application on the RDISK cluster
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
On clustering and retrieval of video shots through temporal slices analysis
IEEE Transactions on Multimedia
Shot clustering techniques for story browsing
IEEE Transactions on Multimedia
Color quantization and processing by Fibonacci lattices
IEEE Transactions on Image Processing
Unsupervised Video Shot Segmentation Using Global Color and Texture Information
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Analysis of Time-multiplexed Security Videos
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Video shot segmentation using graph-based dominant-set clustering
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Action segmentation in dance videos
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT) for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.