Conference Paper


Henri Bouma, Pieter T. Eendebak, Klamer Schutte, George Azzopardi, Gertjan J. Burghouts, “Incremental concept learning with few training examples and hierarchical classification”, Proc. SPIE, vol. 9652, 2015.
[abstract] [pdf] [bib]


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.

Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, the Netherlands