Perceptual multi-channel visual feature fusion for scene categorization

Effectively recognizing sceneries from a variety of categories is an indispensable but challenging technique in computer vision and intelligent systems. In this work, we propose a novel image kernel based on human gaze shifting, aiming at discovering the mechanism of humans perceiving visually/semantically salient regions within a scenery. More specifically, we first design a weakly supervised embedding algorithm which projects the local image features (i.e., graphlets in this work) onto the pre-defined semantic space. Thereby, we describe each graphlet by multiple visual features at both low-level and high-level. It is generally acknowledged that humans attend to only a few regions within a scenery. Thus we formulate a sparsity-constrained graphlet ranking algorithm which incorporates visual clues at both the low-level and the high-level. According to human visual perception, these top-ranked graphlets are either visually or semantically salient. We sequentially connect them into a path which mimics human gaze shifting. Lastly, a so-called gaze shifting kernel (GSK) is calculated based on the learned paths from a collection of scene images. And a kernel SVM is employed for calculating the scene categories. Comprehensive experiments on a series of well-known scene image sets shown the competitiveness and robustness of our GSK. We also demonstrated the high consistency of the predicted path with real human gaze shifting path.