Multi-label Object Attribute Classification using a Convolutional Neural Network

Objects of different classes can be described using a limited number of attributes such as color, shape, pattern, and texture. Learning to detect object attributes instead of only detecting objects can be helpful in dealing with a priori unknown objects. With this inspiration, a deep convolutional neural network for low-level object attribute classification, called the Deep Attribute Network (DAN), is proposed. Since object features are implicitly learned by object recognition networks, one such existing network is modified and fine-tuned for developing DAN. The performance of DAN is evaluated on the ImageNet Attribute and a-Pascal datasets. Experiments show that in comparison with state-of-the-art methods, the proposed model achieves better results.

[1]  Kristen Grauman,et al.  Decorrelating Semantic Visual Attributes by Resisting the Urge to Share , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[3]  Donghoon Lee,et al.  Deep Attribute Networks , 2012, ArXiv.

[4]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[5]  Trevor Darrell,et al.  PANDA: Pose Aligned Networks for Deep Attribute Modeling , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Gang Wang,et al.  Multi-Task CNN Model for Attribute Prediction , 2015, IEEE Transactions on Multimedia.

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  Xiang Bai,et al.  Vehicle Color Recognition With Spatial Pyramid Deep Learning , 2015, IEEE Transactions on Intelligent Transportation Systems.

[10]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[12]  Fei-Fei Li,et al.  Attribute Learning in Large-Scale Datasets , 2010, ECCV Workshops.

[13]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[14]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[15]  Andrew Zisserman,et al.  Learning Visual Attributes , 2007, NIPS.

[16]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[17]  Ahmed M. Elgammal,et al.  Learning Hypergraph-regularized Attribute Predictors , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Jiebo Luo,et al.  Regularized Deep Belief Network for Image Attribute Detection , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Subhransu Maji,et al.  Deep filter banks for texture recognition and segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Wei Xu,et al.  CNN-RNN: A Unified Framework for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).