Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Pattern Spectra for Texture Segmentation of Gray-Scale Images
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
FaceSeg: automatic face segmentation for real-time video
IEEE Transactions on Multimedia
Face and Facial Feature Localization Based on Color Segmentation and Symmetry Transform
MINES '09 Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 02
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Texture Classification from Random Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Texture Segmentation Using Local Spectral Histograms
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Frontal face images are segmented into 7 regions using only sum and difference histograms as pixel information, without any a priori knowledge. In the training phase, a decision tree is created using a projection pursuit algorithm: in each step, the optimal one-dimensional projection is chosen by a simulated annealing process according to a projection index, and classes are isolated by a decision boundary that maximizes class separability, until the end nodes contain only one class each. Satisfactory qualitative and quantitative results were obtained and presented.