Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal on Image and Video Processing - Color in Image and Video Processing
Color quantization and processing by Fibonacci lattices
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
This paper presents an effective algorithm to segment color video into shots for video indexing or retrieval applications. This work adds global texture information to our previous work, which extended the scale-invariant feature transform (SIFT) to color global texture SIFT (CGSIFT). Fibonacci lattice-quantization is used to quantize the image and extract five color features for each region of the image using a symmetrical template. Then, in each region of the image partitioned by the template, the entropy and energy of a co-occurrence matrix are calculated as the texture features. With these global color and texture features, we adopt clustering ensembles to segment video shots. Experimental results show that the additional texture features allow the proposed CGTSIFT algorithm to outperform our previous work, fuzzy-c means, and SOM-based shot detection methods.