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Optimized recognition with few instances based on semantic distance

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Abstract

In this paper, we present a new object recognition model with few instances based on semantic distance. Learning objects with many instances have been studied in computer vision for many years. However, in many cases, not enough positive instances occur, especially for some special categories. We must take full advantage of all instances, including those that do not belong to the category. The main insight is that, given a few positive instances from one category, we can define some other candidate instances as positive instances based on semantic distance to learn this model. Our model responds more strongly to instances with closer semantic distance to positive instances than to instances with farther semantic distance to positive instances. We use a regularized kernel machine algorithm to train the images from the database. The superiority of our method to existing object recognition methods is demonstrated. Experiments using an image database show that our method not only reduces the number of learning instances but also keeps the accurate rate of recognition.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments. This work is supported by the National Key Technology R&D Program of China (2012BAH01F03), National Basic Research (973) Program of China (2011CB302203), research fund of Tsinghua -Tencent Joint Laboratory for Internet Innovation Technology.

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Hao Wu, Zhenjiang Miao, Yi Wang & Manna Lin

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Yi Wang

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  1. Hao Wu
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  2. Zhenjiang Miao
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Correspondence to Hao Wu.

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Wu, H., Miao, Z., Wang, Y. et al. Optimized recognition with few instances based on semantic distance. Vis Comput 31, 367–375 (2015). https://doi.org/10.1007/s00371-014-0931-8

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