|Table of Contents|

Pose-Based Discriminative-Attributes Learning for Fine-Grained Recognition(PDF)

《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年01期
Page:
65-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Pose-Based Discriminative-Attributes Learning for Fine-Grained Recognition
Author(s):
Song FengyiZhang ShoudongYang Ming
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
attribute learningdiscriminative attributedistributed representationfine-grained recognition
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2017.01.010
Abstract:
Commonly existed various posture of object makes great challenges for object recognition in computer vision literature. Attribute representation shows robust describable ability with clear semantic meaning invariant to changes of environment factors including posture. However,the inherent description advantages of attributes also result big challenges for itself to learn well worked attribute predictor. Consequently,the key issues in attribute learning are to alleviate the difficulty of predicting attributes and enhance the discriminant ability at the mean time,which especially important for fine-grained recognition task. By explicitly modeling the posture states and learning discriminative attribute with respect to different postures,describable and discriminative attribute can be built for final category recognition. The proposed pose-based discriminative attribute is verified on publicly available fine-grained dataset CUB with advanced performance.

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