Image Clustering Using Discriminant Image Features
- Resource Type
- Conference
- Authors
- Ahmed, Nasir; Jalil, Abdul
- Source
- 2013 11th International Conference on Frontiers of Information Technology Frontiers of Information Technology (FIT), 2013 11th International Conference on. :31-36 Dec, 2013
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Principal component analysis
Feature extraction
Image databases
Clustering algorithms
Vectors
Manifolds
image clustering
manifold learning
two-dimensional two-directional PCA
image feature extraction
- Language
Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Usually, gray level image features are used that are obtained by resizing original images through linear interpolation approach. However, significant image variance information is lost in gray level image features. Clustering models that are based on discriminant analysis can be made more effective in principal component analysis (PCA) space whereas leading projection vectors contain significant image variance information. For optimal clustering performance, we used two-dimensional two-directional PCA technique to extract significant image features. We report clustering performance of Spectral Embedded Clustering (SEC) model using discriminant image features on 6 benchmark image databases. Clustering performance is compared with existing state-of-art clustering approaches. Significant overall performance improvement is observed using proposed discriminant image features over gray level image features.