Research of fusing image classification into object localization.

In order to improve the performance of the object localization technology, this paper researched the approach of fusing image classification into object localization. According to the object localization task in the large-scaled image set containing multi-class objects, this paper proposed an effective scheme that a fast image classification was carried out before a precise object localization. The MIMLSVM + algorithm predicted which images contained the objects, then the ESS method localized the objects just in those images. And aiming at a higher precision in the object localization task, the paper proposed a new scoring mechanism of the optimal box which included the global classified information in the multi-label classification task. The corresponding experimental results on the PASCAL 2006 data set show that the former method shortens the processing time while obtaing a good average positioning accuracy; the latter method brings an improvement in the localization performance on many categories as it make the scoring mechanism of the optimal box better. The experimental results prove that fusing image classification into object localization can really improve the performance of object localization. [ABSTRACT FROM AUTHOR]