Elisavet Chatzilari
Spiros Nikolopoulos
Yiannis Kompatsiaris
ABSTRACT
The widespread adoption of Web 2.0 applications has resulted in the creation of huge amounts of user-generated multimedia content, a fact that motivated the investigation of employing this content for training. However, the nature of these annotations (i.e. global level) and the noise existing in the associated information, as well as the ambiguity that characterizes these examples disqualifies them from being directly appropriate learning samples. Nevertheless, the tremendous volume of data that is currently hosted in social networks gives us the luxury to disregard a substantial number of candidate learning examples, provided we can devise a gauging mechanism that could filter out any ambiguous or noisy samples. Our objective in this work is to define a measure for visual ambiguity, which is caused by the visual similarity of semantically dissimilar concepts, in order to help in the process of selecting positive training regions from user tagged images. This is done by limiting the search space of the potential images to the ones yielding a higher probability to contain the desired regions, while at the same time not including visually ambiguous objects that could confuse the selection algorithm. Experimental results show that the employment of visual ambiguity allows for better separation between the targeted true positive and the undesired negative regions.
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Digital Library
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Index Terms
- Towards modelling visual ambiguity for visual object detection
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