A new paper “Image Classification via Object-aware Holistic Superpixel Selection” from VIM PI, Prof. Wang is accepted by (TIP).

In this paper, we propose an object-aware Holistic superPixel Selection (HPS) method to automatically select the discriminative superpixels of an image for image classification purpose. Through only considering the selected superpixels, the interference of cluttered background on the object can be alleviated effectively and thus the classification performance is significantly enhanced. In particular, for an image, HPS first selects the discriminative superpixels for the characteristics of certain class, which can together match the object “template” of this class well. And then these superpixels compose a class-specific matching region. By performing such superpixel selection for several most probable classes respectively, HPS generates multiple class-specific matching regions for a single image. Then HPS merges these matching regions into an integral object region through exploiting their pixel-level intersection information. Finally such object region instead of the original image is used for image classification. An appealing advantage of HPS is the ability to alleviate the interference of cluttered background yet not require the object to be segmented out accurately. We evaluate the proposed HPS on four challenging image classification benchmark datasets: Oxford-IIIT PET 37, Caltech-UCSD Birds 200, Caltech 101, and PASCAL VOC 2011. The experimental results consistently show that the proposed HPS can remarkably improve the classification performance.

Attention: VIMers, please work harder and get your own achievements.