|Table of Contents|

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

《南京师大学报（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 Fengyi; Zhang Shoudong; Yang Ming
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
attribute learning; discriminative attribute; distributed representation; fine-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.

References:

[1] SHAN S,CHEN X,GAO W. Face misalignment problem[M]. New York,USA:Springer,2009.
[2]ZHU X,LEI Z,LIU X,et al. Face alignment across large poses:a 3D solution[C]//Proceedings of the 29th IEEE Computer Vision and Pattern Recogntion. Las Vegas,USA:IEEE,2016.
[3]JOURABLOO A,LIU X. Large-pose face alignment via CNN-based dense 3D model fitting[C]//Proceedings of the 29th IEEE Computer Vision and Pattern Recogntion. Las Vegas,USA:IEEE,2016.
[4]JAYARAMAN D,SHA F,GRAUMAN K,et al. Decorrelating semantic visual attributes by resisting the urge to share[C]//Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition. Columbus,USA:IEEE,2014.
[5]GAVVES E,FERNANDO B,SNOEK C G,et al. Local alignments for fine-grained categorization[J]. International journal of computer vision,2015,111(2):191-212.
[6]ZHANG L,YANG Y,ZIMMERMANN R,et al. Fine-grained image categorization by localizing tiny object parts from unannotated images[C]//Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. Shanghai:ACM,2015.
[7]WAH C,BRANSON S,WELINDER P,et al. The caltech-UCSD birds-200-2011 dataset[R]. Los Angeles:CIT,2011.
[8]LAMPERT C H,NICKISCH H,HARMELING S,et al. Learning to detect unseen object classes by between-class attribute transfer[C]//Proceedings of the 22th IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA:IEEE,2009.
[9]BERG T,BELHUMEUR P N. Tom-vs-pete classifiers and identity-preserving alignment for face verification[C]//Proceedings of the 23rd British Machine Vision Conference. Guildford,UK:IEEE,2012.
[10]BERG T,BELHUMEUR P N. POOF:part-based one-vs.-one features for fine-grained categorization,face verification,and attribute estimation[C]//Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. Portland,USA:IEEE,2013.
[11]BOURDEV L,MAJI S,MALIK J,et al. Poselets:A distributed representation for visual recognition[J]. Journal of vision,2011,11(11):891-891.
[12]REED J L,PALSSON B O. Thirteen years of building constraint-based in silico models of Escherichia coli[J]. Journal of bacteriology,2003,185(9):2 692-2 699.
[13]FELZENSZWALB P F,GIRSHICK R,MCALLESTER D,et al. Object detection with discriminatively trained part-based models[J]. IEEE transactions on pattern analysis and machine intelligence,2010,32(9):1 627-1 645.
[14]LFARNED-MILLER E,HUANG G B,ROYCHOWDHURY A,et al. Labeled faces in the wild:a survey[M]//Advances in face detection and facial image analysis. Berlin:Springer,2016:189-248.
[15]KRISHNA R,ZHU Y,GROTH O,et al. Visual genome:connecting language and vision using crowdsourced dense image annotations[J]. International journal of computer vision,2017,23(1):1-42.
[16]WANG S,JOO J,WANG Y,et al. Weakly supervised learning for attribute localization in outdoor scenes[C]//Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. Portland,USA:IEEE,2013.
[17]WANG Y,MORI G. A discriminative latent model of object classes and attributes[C]//Proceedings of the 11th IEEE Conference on European Conference on Computer Vision. Greece:IEEE,2010.
[18]WANG Y,CHOI J,MORARIU V I,et al. Mining discriminative triplets of patches for fine-grained classification[C]//Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016.
[19]HAN Z,TAO X,MOHAMED E,et al. SPDA-CNN:unifying semantic part detection and abstraction for fine-grained recognition[C]//Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016.
[20]AKATA Z,MALINOWSKI M,FRITZ M,et al. Multi-cue zero-shot learning with strong supervision[C]//Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016.
[21]REED S,AKATA Z,SCHIELE B,et al. Learning deep representations of fine-grained visual descriptions[C]//Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016.
[22]CUI Y,ZHOU F,LIN Y,et al. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop[C]//Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016.
[23]FELZENSZWALB P F,HUTTENLOCHER D P. Pictorial structures for object recognition[J]. International journal of computer vision,2005,61(1):55-79.
[24]TAN X,SONG F,ZHOU Z,et al. Enhanced pictorial structures for precise eye localization under incontrolled conditions[C]//Proceedings of the 22th IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA:IEEE,2009.
[25]ZHANG N,FARRELL R,IANDOLA F,et al. Deformable part descriptors for fine-grained recognition and attribute prediction[C]//Proceedings of IEEE Conference on International Conference on Computer Vision. Sydney,Australia:IEEE,2013:729-736.
[26]KRAUSE J,GEBRU T,DENG J,et al. Learning features and parts for fine-grained recognition[C]//Proceedings of the 27th IEEE Conference on International Conference on Pattern Recognition. Columbus,USA:IEEE,2014.
[27]KRAUSE J,JIN H,YANG J,et al. Fine-grained recognition without part annotations[C]//Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition. Boston,USA:IEEE,2015.
[28]DENG J,DONG W,SOCHER R,et al. ImageNet:a large-scale hierarchical image database[C]//Proceedings of the 22th IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA:IEEE,2009.
[29]FARHADI A,ENDRES I,HOIEM D,et al. Describing objects by their attributes[C]//Proceedings of the 22th IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA:IEEE,2009.

Memo

Memo:
-
Last Update: 1900-01-01