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Proceedings Paper

Incremental concept learning with few training examples and hierarchical classification
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Paper Abstract

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classifier scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classification. We introduce a new dataset, which we call TOSO, and use it to demonstrate the effectiveness of the proposed method for the localization and recognition of multiple objects in images.

Paper Details

Date Published: 21 October 2015
PDF: 8 pages
Proc. SPIE 9652, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII, 96520E (21 October 2015); doi: 10.1117/12.2194438
Show Author Affiliations
Henri Bouma, TNO (Netherlands)
Pieter T. Eendebak, TNO (Netherlands)
Klamer Schutte, TNO (Netherlands)
George Azzopardi, TNO (Netherlands)
Gertjan J. Burghouts, TNO (Netherlands)


Published in SPIE Proceedings Vol. 9652:
Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII
Roberto Zamboni; Douglas Burgess; Gari Owen; François Kajzar; Attila A. Szep; Harbinder Rana, Editor(s)

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