Autoencoder Based Dimensionality Reduction of Feature Vectors for Object Recognition.
Reyhan Kevser KeserBehçet Ugur TöreyinPublished in: SITIS (2019)
Keyphrases
- feature vectors
- dimensionality reduction
- object recognition
- feature extraction
- feature space
- euclidean distance
- image features
- principal component analysis
- computer vision
- data representation
- unsupervised learning
- high dimensional
- high dimensionality
- low dimensional
- high dimensional data
- pattern recognition
- d objects
- image understanding
- linear discriminant analysis
- object categories
- template matching
- texture features
- gaussian mixture model
- metric learning
- face images
- nonlinear dimensionality reduction
- natural images
- linear dimensionality reduction
- similarity measure
- support vector machine
- visual recognition
- constrained search
- data points
- pattern recognition and machine learning
- image classification
- object class
- image representation
- manifold learning
- preprocessing step
- rotation invariant
- structure preserving
- graph embedding
- kernel learning
- multiscale
- restricted boltzmann machine
- face recognition
- object categorization
- image processing
- neural network
- shape descriptors