Joint image representation and classification in random semantic spaces

• We jointly consider image representation and classification in unified framework.
• Images are randomly selected for semantic space construction by training classifiers.
• We use random semantic spaces for image representation and class prediction.
• We achieve the state-of-the-art performance on several public datasets.

Local feature based image representation has been widely used for image classification in recent years. Although this strategy has been proven very effective, the image representation and classification processes are relatively independent. This means the image classification performance may be hindered by the representation efficiency. To jointly consider the image representation and classification in an unified framework, in this paper, we propose a novel algorithm by combining image representation and classification in the random semantic spaces. First, we encode local features with the sparse coding technique and use the encoding parameters for raw image representation. These image representations are then randomly selected to generate the random semantic spaces and images are then mapped to these random semantic spaces by classifier training. The mapped semantic representation is then used as the final image representation. In this way, we are able to jointly consider the image representation and classification in order to achieve better performances. We evaluate the performances of the proposed method on several public image datasets and experimental results prove the proposed method׳s effectiveness.