We present a method for generic visual categorization. This technique exploits an analogy with learning methods for text categorization based on the simple bag of words approach. Two key novel aspects of this approach are that it handles multiple image categories simultaneously and that it is intrinsically invariant to affine image transformations. Results are presented for simultaneously classifying seven semantic visual categories using Naive Bayes techniques.
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Visual Categorization with Bags of Keypoints
at
| Christopher Dance, Jutta Willamowski, Lixin Fan, Cedric Bray, Gabriela Csurka |
| ECCV International Workshop on Statistical Learning in Computer Vision, Prague, 2004. |
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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