Extracting shared subspace incrementally for multi-label image classification

Abstract

With the popularity of internet technology, thousands of new images with multiple labels appear on the web every day. For a large number of images updated daily on the websites, it is of ever-increasing importance to classify these new multi-label images online in real time. Accordingly, this paper presents an incremental shared subspace learning method for multi-label image classification. With the incremental lossless matrix factorization, the proposed algorithm can be incrementally performed without using original existing input data, thus high computational complexity involved in extracting the shared subspace can be avoided. Several publicly available multi-label image datasets are used to evaluate the proposed method. Experimental results demonstrate that the proposed approach is much more efficient than the non-incremental methods without decreasing the classification performance.