Title:
Orientation-Aware Scene Understanding for Mobile Cameras

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Wang, Jing
Schindler, Grant
Essa, Irfan
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Abstract
We present a novel approach that allows anyone to quickly teach their smartphone how to understand the visual world around them. We achieve this visual scene understanding by leveraging a camera-phone’s inertial sensors to lead to both a faster and more accurate automatic labeling of the regions of an image into semantic classes (e.g. sky, tree, building). We focus on letting a user train our system from scratch while out in the real world by annotating image regions in situ as training images are captured on a mobile device, making it possible to recognize new environments and new semantic classes on the fly. We show that our approach outperforms existing methods, while at the same time performing data collection, annotation, feature extraction, and image segment classification all on the same mobile device.
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2012-09
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