Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Robust self-tuning semi-supervised learning
Neurocomputing
Sparse regularization for semi-supervised classification
Pattern Recognition
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
Combining randomization and discrimination for fine-grained image categorization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Beyond spatial pyramids: Receptive field learning for pooled image features
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Image classification, namely classifying thousands of images into different classes, is an important task in images organization. Although many existing methods attempt to address this task, most of those are proposed in a supervised way based on the labeled data. However, in real world the labeled data is usually hard to obtain while large amounts of unlabeled data can be easier to acquire. The problem of effectively and efficiently classifying images combining unlabeled data with labeled data remains pretty much open. To this end, in this paper we proposed a novel semi-supervised image classification method based on sparse coding spatial pyramid matching (ScSPM). Specifically, we use the unsupervised ScSPM method to get the representation of unlabeled images as like the labeled images. Based on the obtained image representation, we then propose a linear LapSVM as the semi-supervised classifier. Since the proposed method has a linear kernel and can effectively explore the intrinsic structure of data by making full use of the information of unlabeled data, it leads to more accurate and efficient image classification. Experimental results on two real world datasets demonstrate the effectiveness of our method especially when the labeled data is very little.