Semi-supervised Sparse Representation with Graph Regularization for Image Classification
Image classification is a challenging problem for computer in reality. Large numbers of methods can achieve satisfying performances with sufficient labeled images. However, labeled images are still highly limited for certain image classification tasks. Instead, lots of unlabeled images are available and easy to be obtained. Therefore, making full use of the available unlabeled data can be a potential way to further improve the performance of current image classification methods. In this paper, we propose a discriminative semi-supervised sparse representation algorithm for image classification. In the algorithm, the classification process is combined with the sparse coding to learn a data-driven linear classifier. To obtain discriminative predictions, the predicted labels are regularized with three graphs, i.e., the global manifold structure graph, the within-class graph and the between-classes graph. The constructed graphs are able to extract structure information included in both the labeled and unlabeled data. Moreover, the proposed method is extended to a kernel version for dealing with data that cannot be linearly classified. Accordingly, efficient algorithms are developed to solve the corresponding optimization problems. Experimental results on several challenging databases demonstrate that the proposed algorithm achieves excellent performances compared with related popular methods.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral Classificationimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Semi-supervised Data Representation via Affinity Graph Learning
We consider the general problem of utilizing both labeled and unlabeled data to improve data representation performance. A new semi-supervised learning framework is proposed by combing manifold regularization and data re…
Graph LearningSemi-supervised dictionary learning with graph regularization and active points
Supervised Dictionary Learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of la…
Dictionary LearningGeneral Classificationimage-classificationImage ClassificationCongestion Forecast for Trains with Railroad-Graph-based Semi-Supervised Learning using Sparse Passenger Reports
Forecasting rail congestion is crucial for efficient mobility in transport systems. We present rail congestion forecasting using reports from passengers collected through a transit application. Although reports from pass…
Efficiently Learning the Graph for Semi-supervised Learning
Computational efficiency is a major bottleneck in using classic graph-based approaches for semi-supervised learning on datasets with a large number of unlabeled examples. Known techniques to improve efficiency typically …
Computational EfficiencyLocal High-order Regularization on Data Manifolds
The common graph Laplacian regularizer is well-established in semi-supervised learning and spectral dimensionality reduction. However, as a first-order regularizer, it can lead to degenerate functions in high-dimensional…
Dimensionality ReductionVocal Bursts Intensity Prediction