Image splicing detection with principal component analysis generated low-dimensional homogeneous feature set based on local binary pattern and support vector machine.
Debjit DasRuchira NaskarRajat Subhra ChakrabortyPublished in: Multim. Tools Appl. (2023)
Keyphrases
- feature set
- low dimensional
- feature representation
- principal component analysis
- principal components
- local binary pattern
- feature space
- texture features
- feature selection
- feature extraction
- feature vectors
- support vector machine
- multiscale
- texture classification
- dimensionality reduction
- image features
- input image
- face recognition
- image descriptors
- spatial information
- image analysis
- image classification
- image retrieval
- single image
- rotation invariant
- feature reduction
- high dimensional
- classification accuracy
- random forest
- set of real images
- kernel function
- image segmentation
- selected features
- object detection
- texture analysis
- svm classifier
- computer vision
- texture descriptors
- detection method
- data points
- post processing
- feature descriptors
- detection algorithm
- video sequences
- textural features
- linear discriminant analysis
- gray level co occurrence matrix
- gaussian mixture model