Review: Sparse coding and classifier ensemble based multi-instance learning for image categorization

Authors:
Xiangfa Song;L. C. Jiao;Shuyuan Yang;Xiangrong Zhang;Fanhua Shang
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Mailbox 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Mailbox 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Mailbox 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Mailbox 224, No. 2 South Taibai Road, Xi'an 710071, PR China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Mailbox 224, No. 2 South Taibai Road, Xi'an 710071, PR China
Venue:
Signal Processing
Year:
2013

Citing 29
Cited 0

Solving the multiple instance problem with axis-parallel rectangles

Artificial Intelligence
A framework for multiple-instance learning

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Ensembling neural networks: many could be better than all

Artificial Intelligence
Content-Based Image Retrieval Using Multiple-Instance Learning

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multi-Instance Kernels

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Image Database Retrieval with Multiple-Instance Learning Techniques

ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Image Categorization by Learning and Reasoning with Regions

The Journal of Machine Learning Research
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multi-instance problems with classifier ensemble based on constructive clustering

Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Robust Object Recognition with Cortex-Like Mechanisms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-taught learning: transfer learning from unlabeled data

Proceedings of the 24th international conference on Machine learning
On the relation between multi-instance learning and semi-supervised learning

Proceedings of the 24th international conference on Machine learning
A robust color object analysis approach to efficient image retrieval

EURASIP Journal on Applied Signal Processing
Multi-instance learning by treating instances as non-I.I.D. samples

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Marginalized multi-instance kernels

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Online Learning for Matrix Factorization and Sparse Coding

The Journal of Machine Learning Research
LSA based multi-instance learning algorithm for image retrieval

Signal Processing
Multi-stage image denoising based on correlation coefficient matching and sparse dictionary pruning

Signal Processing
Multi-instance multi-label learning

Artificial Intelligence
Sparsity-based image denoising via dictionary learning and structural clustering

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

IEEE Transactions on Signal Processing
Asymptotically optimal block quantization

IEEE Transactions on Information Theory
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

In this paper, we propose a novel method based on sparse coding and classifier ensemble for tackling image categorization problem under the framework of multi-instance learning (MIL). Specifically, a dictionary is learned from the instances of all the training bags. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is also represented one feature vector which is achieved via sparse representations of all instances within the bag. Thus, the MIL problem is converted to a single-instance learning problem that can be solved by well-know single-instance learning methods, such as support vector machines (SVMs). Two strategies are used to improve classification performance: first, the component classifiers are obtained by repeatedly using the above method with dictionaries of different sizes; second, the result of classifier ensemble is used for prediction. Experimental results on the COREL data sets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.