Learning sparse features on-line for image classification

Authors:
Ziming Zhang;Jiawei Huang;Ze-Nian Li
Affiliations:
School of Technology, Oxford Brookes University, Oxford, UK;School of Computing Science, Simon Fraser University, B.C., Canada;School of Computing Science, Simon Fraser University, B.C., Canada
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

Citing 8
Cited 0

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Feature Selection Newton Method for Support Vector Machine Classification

Computational Optimization and Applications
A Sparse Support Vector Machine Approach to Region-Based Image Categorization

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scene Classification Using a Hybrid Generative/Discriminative Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Correspondence Via Graph Matching: Models and Global Optimization

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Evaluating Color Descriptors for Object and Scene Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose an efficient sparse feature on-line learning approach for image classification. A large-margin formulation solved by linear programming is adopted to learn sparse features on the max-similarity based image representation. The margins between the training images and the query images can be directly utilized for classification by the Naive-Bayes or the K Nearest Neighbor category classifier. Balancing between efficiency and classification accuracy is the most attractive characteristic of our approach. Efficiency lies in its on-line sparsity learning algorithm and direct usage of margins, while accuracy depends on the discriminative power of selected sparse features with their weights. We test our approach using much fewer features on Caltech-101 and Scene-15 datasets and our classification results are comparable to the state-of-the-art.