Object proposal algorithms are needed to find bounding boxes for salient class-agnostic objects. It is an important pre-processing step for object detection in the wild. While most state-of-the-art object detection methods adopt an end-to-end deep neural networks, we aim at an independent object proposal unit that has low complexity and high performance. The proposed light-weight object proposal can be combined with any classification process to reduce model and computation complexity.

Our current method is built upon the PixelHop framework, it is called OPPHop (Object Proposal PixelHop). The term “hop” denotes the neighborhood of a pixel. OPPHop conducts spectral analysis on neighborhoods of different sizes centered on a pixel through a sequence of cascaded dimension reduction units. The neighborhoods of an object contain salient contours and, as a result, they have distinctive spectral signatures at a certain scale that matches the object size. The distinctive regions can be predicted based on supervised learning with Saab coefficients as the input.


— by Hongyu Fu