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Author (up) Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom edit   pdf
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  Title Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains Type Conference Article
  Year 2015 Publication International Conference on Intelligent Robots and Systems Abbreviated Journal  
  Volume Issue Pages 2488 - 2495  
  Keywords Visual Learning; Computer Vision; Autonomous Agents  
  Abstract In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks.  
  Address Hamburg; Germany; October 2015  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference IROS  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ OSL2015 Serial 2664  
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