Computer Vision Lab - Object Recognition

Object recognition is one of the vision applications that deal with data of multiple classes. In such a case, not only discriminative features but also the class-specific features should be considered because the goodness of a feature representation for recognition is often category-dependent, and can even be object-dependent for the case of large intra-class variation. Thus, we aim to improve recognition accuracy by focusing on feature representation in terms of image features and similarity measures, where various feature representations in the domain of kernel matrices are fused to alleviate the difficulties caused by diverse forms of them.

Object Recognition with Kernel Machines

The problems and observations
- Diverse object categories
- Large intra-class variations
- The goodness of a feature representation for recognition is often category-dependent, and can even be object-dependent for the case of large intra-class variation
Improve recognition accuracy by focusing on
- Image features
- Similarity measures
Approach
- Fusing various feature representations in the domain of kernel matrices
- Learning a local ensemble kernel machine (e.g. local ensemble kernel + SVM) for each training sample
  - Localized kernel alignment (initialization)
  - MRF modeling and optimization
  - Associate each sample with a SVM classifier
Feature Fusion and Kernel Alignment
- Fusing feature representations is now combining kernels
- The effectiveness of a possible fusion can therefore be reasonably estimated by how good an ensemble kernel is
- We consider target kernel alignment for measuring the goodness of a kernel. [Cristianini et al. '01]
Optimization over a MRF Model
- Retain the effectiveness of each local classifier
- Alleviate the possible overfitting
- Reduce the redundancy of the local classifiers

Multiple Kernel Learning (MKL) for Dimensionality Reduction

Observations
- No single feature representation suffices to explain the complexity of the whole data
Goals
- Perform MKL for heterogeneous feature fusion
- Generalize a set of dimensionality reduction methods to consider multiple kernels
- Extend MKL from supervised learning to unsupervised and semi-supervised learning
- Improve performances of vision applications by using multiple feature representations