Image classification using Harr-like transformation of local features with coding residuals

Recently, the bag-of-visual-words (BoW) model has been proven very effective for image classification. However, most researchers used local features directly while neglecting their spatial information and correlations. Besides, the encoding of local features causes some information loss which also hinders the final image classification performance. To tackle these problems, in this paper, we proposed a novel image classification method using Harr-like transformation of local features with additional consideration of coding residuals. We apply Harr-like transformation on local features to combine the spatial information as well as the correlations of local features. These Harr-like transformed local features are then encoded using non-negative sparse coding. We jointly consider the coding parameters and the coding residuals as the local representation in order to reduce the information loss during the local feature encoding process. Experiments on several public datasets demonstrate the effectiveness of the proposed method.

► We propose an image classification method using Harr-like transformation with coding residuals.
► Harr-like transformation combines spatial information and correlations of local features.
► We use non-negative sparse coding with residuals to alleviate information loss.
► We achieve the state-of-the-art performance on several well-known datasets.