Large-scale image classification using fast SVM with deep quasi-linear kernel

In this paper, a novel fast support vector machine (SVM) method combining with the deep quasi-linear kernel (DQLK) learning is proposed for large scale image classification. This method can train large-scale dataset with SVM fast using less memory space and less training time. Since SVM classifiers are constructed by support vectors (SVs) that lie close to the separation boundary, removing the other samples that are not relevant to SVs has no effect on building the separation boundary. In other word, we need to reserve the boundary samples that are likely to be SVs. The proposed method uses an approximate separation classifier obtained by training a small subset selected from training data randomly as a reference to detect and remove non-relevant samples whose normalized algebraic distance to the reference classification boundary is larger than a threshold. The proposed method is implemented in the feature space. Therefore, by means of a good kernel method the proposed method can train high dimension data and image data. The DQLK method is used to extract and construct kernel matrix for the proposed method. Experimental results on different datasets and expended very large scale datasets show that the proposed method obtains outstanding ability to deal with very large scale image classification.