Object proposals using CNN-based edge filtering

With the success of deep learning in the last few years, the object detection community shifted from processing on exhaustive sliding windows to smaller set of object proposals using more powerful and deep visual representations. Object proposals increase the accuracy and speed up detection process by reducing the search space. In this paper we propose a novel idea of filtering irrelevant edges using semantic image filtering and true objectness learnt within convolutional layers of CNN. Our approach localizes well proposals by producing highly accurate bounding boxes and reduces the number of proposals. The greatest benefit of our approach is that it can be integrated into any existing method exploiting edge-based objectness to achieve consistently high recall across various intersection over union thresholds. Unlike other supervised methods, our approach does not require bounding box annotations for training. Experiments on PASCAL VOC 2007 dataset demonstrate that our approach improves the state-of-the-art model with a significant margin.