Abstract
This paper presents a novel research on promoting the performance and enriching the functionalities of object recognition. Instead of simply fitting various data to a few predefined semantic object categories, we propose to generate proper results for different object instances based on their actual visual appearances. The results can be fine-grained and layered categorization along with absolute or relative localization. We present a generic model based on structured prediction and an efficient online learning algorithm to solve it. Experiments on a new benchmark dataset demonstrate the effectiveness of our model and its superiority against traditional recognition methods.
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More From: International Journal of Pattern Recognition and Artificial Intelligence
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