In a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different approaches. These features are often separately selected and learned by machine learning methods. In this paper, the relation between distinct features obtained by different feature extraction approaches from the same original images was studied by Kernel Canonical Correlation Analysis (KCCA). We apply a Support Vector Machine (SVM) classifier in the learnt semantic space of the combined features and compare against SVM on the raw data and previously published state-of-the-art results. Experiment show that significant improvement is achieved with the SVM in the semantic space in comparison with direct SVM classification on the raw data.