Orthogonal and Smooth Subspace Based on Sparse Coding for Image Classification

Many real-world problems usually deal with high-dimensional data, such as images, videos, text, web documents and so on. In fact, the classification algorithms used to process these high-dimensional data often suffer from the low accuracy and high computational complexity. Therefore, we propose a framework of transforming images from a high-dimensional image space to a low-dimensional target image space, based on learning an orthogonal smooth subspace for the SIFT sparse codes (SC-OSS). It is a two stage framework for subspace learning. Firstly, a sparse coding followed by spatial pyramid max pooling is used to get the image representation. Then, the image descriptor is mapped into an orthonormal and smooth subspace to classify images in low dimension. The proposed algorithm adds the orthogonality and a Laplacian smoothing penalty to constrain the projective function coefficient to be orthogonal and spatially smooth. The experimental results on the public datasets have shown that the proposed algorithm outperforms other subspace methods.