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Robust Object Recognition via Visual Pathway Feedback
Chong Wang; Junge Zhang; Peipei Yang; Kaiqi Huang
Conference NameInternational Conference on Pattern Recognition (ICPR)
Source PublicationICPR
Conference Date2014
Conference PlaceStockholm,Sweden
AbstractObject recognition, which consists of classification
and detection, has two important attributes for robustness: (1)

Closeness: detection windows should be close to object locations,
and (2)
Adaptiveness: object matching should be adaptive to
object variations in classification. It is difficult to satisfy both
attributes by considering classification and detection separately,
thus recent studies combine them based on confidence contextualization and foreground modeling. However, these combinations
neglect feature saliency and object structure, which are important
for recognition. In fact, object recognition originates in the
mechanism of “what” and “where” pathways in human visual
systems, and more importantly, these pathways have feedback to
each other, which provides a probable way to improve closeness
and adaptiveness. Inspired by the feedback, we propose a robust
object recognition framework by designing a computational
model of the feedback mechanism. In the “what” feedback, the
feature saliency from classification is exploited to rectify detection
windows for better closeness; while in the “where” feedback,
object parts from detection are used to model object matching of
object structure for better adaptiveness. Experiments show that
the “what” and “where” feedback can be effective to improve
closeness and adaptiveness for robust object recognition, and
encouraging results are obtained on the challenging PASCAL
VOC 2007 dataset

KeywordObject Recognition Bag Of Words Visual Pathway Feedback
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
GB/T 7714
Chong Wang,Junge Zhang,Peipei Yang,et al. Robust Object Recognition via Visual Pathway Feedback[C],2014.
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