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, here 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 object proposal method is built upon the PixelHop framework, it is called OPPHop (Object Proposal PixelHop). The term “hop” denotes the neighborhood of a pixel. The high-level principle of our method is described below. OPPHop conducts spectral analysis on neighborhoods of different sizes centered on a pixel through a sequence of cascaded dimension reduction units. The spectral analysis tool is called the Saab (Subspace approximation with adjusted bias) transform. 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.
The proposed OPPHop method consists of the following three steps: 
1. From fine-to-coarse spatial resolutions, conduct successive Saab transforms for spectral representation of neighborhoods of various sizes.
2. Predict distinctive spatial regions in various spatial resolutions via supervised learning.
3. Fuse distinctive regions of different spatial resolutions via image priors.
We evaluate OPPHop framework on the popular PASCAL VOC 2007 dataset using conventional accuracy metrics such as the Intersection over Union (IoU), the area under the curve (AUC), and the proposal recall under various threshold values. We conduct performance benchmarking with the well-known Selective Search and Edge Box methods. Furthermore, we compare their requirements in terms of the number of proposals, computational complexity and model sizes to achieve such accuracy. Finally, we demonstrate by experiments that OPPHop outperforms Selective Search and Edge Box in both efficiency and accuracy.

OPPHop: A Light-Weight High-Performance Object Proposal Method

This is the 4th of four poster sessions.

**Date & Time:**  Thursday March 25th,  12-1:30pm PDT

**Location:** Poster Session Room 2B

The live poster sessions will happen in our Gather.Town lounge at the designated time. Each session has their own room. Once you click the gathertown button below, you will need to navigate to the Poster Session Room for each session located on the right side of the lounge. Each speaker will be by their poster where you can communicate with them and view their poster by clicking X on your keyboard.

[![](https://assets.underline.io/uploads/markdown_image/1/image/737a34c989ecf3ae95182670d99c072c.png)](https://gather.town/app/5IAViMZXC9ZF4E4l/NAML)

Poster Session 2B

poster

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Hongyu Fu

1

SHORT BIO

Presentations

OPPHop: A Light-Weight High-Performance Object Proposal Method

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES