A Fast Data Collection and Augmentation Procedure for Object Recognition

When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or failure of the system. However, it is typically extremely laborintensive to collect data, as the process usually involves acquiring the image, then manual cropping and hand-labeling. Preparing large training sets for object recognition has already become one of the main bottlenecks for such emerging applications as mobile robotics and object recognition on the web. This paper focuses on a novel and practical solution to the dataset collection problem. Our method is based on using a green screen to rapidly collect example images; we then use a probabilistic model to rapidly synthesize a much larger training set that attempts to capture desired invariants in the object's foreground and background. We demonstrate this procedure on our own mobile robotics platform, where we achieve 135x savings in the time/effo...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Benjamin Sapp, Ashutosh Saxena, Andrew Y. Ng
• Keywords: AAAI 2008, Intelligent Agents, Object Class Recognition, Object Recognition, Training Set