Active Classification of Large 3D Shape Collection

To efficiently and accurately classify a large 3D shape collection, this paper proposes a novel interactive system by incorporating active learning, online learning and user intervention. Given a shape collection, our system iteratively alternates the interactive annotation and verification until all the shapes are classified. The main advantage is that it provides faster interactive classification rates than alternative approaches. Our system achieves this goal by a unified active learning algorithm that selects the shapes to be annotated or verified, which requires a probability model for simulating the time cost of human input during manual intervention. After manually classifying these selected shapes, we use an extended soft confidence-weighted learning method to update the classifier incrementally and efficiently for the subsequent active selection and shape classification in turn. Experimental results demonstrated the effectiveness of the proposed method.

[1]  W. Welch.  Algorithmic complexity: three NP- hard problems in computational statistics , 1982 .

[2]  Bernard Dubuisson,et al.  A statistical decision rule with incomplete knowledge about classes , 1993, Pattern Recognit..

[3]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[4]  Xiaowei Xu,et al.  Representative Sampling for Text Classification Using Support Vector Machines , 2003, ECIR.

[5]  Outlier detection using k-nearest neighbour graph , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  Trevor Darrell,et al.  Active Learning with Gaussian Processes for Object Categorization , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  A. Krishnakumar.  Active Learning Literature Survey , 2007 .

[8]  Andrea Fusiello,et al.  The bag of words approach for retrieval and categorization of 3D objects , 2010, The Visual Computer.

[9]  Giuseppe Patanè,et al.  Spectral feature selection for shape characterization and classification , 2011, The Visual Computer.

[10]  Daniel Cohen-Or,et al.  Active co-analysis of a set of shapes , 2012, ACM Trans. Graph..

[11]  Steven C. H. Hoi,et al.  Exact Soft Confidence-Weighted Learning , 2012, ICML.

[12]  Joachim Denzler,et al.  Kernel Null Space Methods for Novelty Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Gabriela Csurka,et al.  Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Saturnino Maldonado-Bascón,et al.  Evaluating 3D spatial pyramids for classifying 3D shapes , 2013, Comput. Graph..

[15]  Dong-Hyun Lee,et al.  Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .

[16]  Leonidas J. Guibas,et al.  Fine-grained semi-supervised labeling of large shape collections , 2013, ACM Trans. Graph..

[17]  Olivier Colot,et al.  A parts-based approach for automatic 3D shape categorization using belief functions , 2013, TIST.

[18]  Vincent Barra,et al.  3D shape retrieval and classification using multiple kernel learning on extended Reeb graphs , 2014, The Visual Computer.

[19]  Aleksey Boyko,et al.  Cheaper by the dozen: group annotation of 3D data , 2014, UIST.

[20]  Subhransu Maji,et al.  Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Fei-Fei Li,et al.  Best of both worlds: Human-machine collaboration for object annotation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Jianxiong Xiao,et al.  3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Kai Liu,et al.  Iterative 3D shape classification by online metric learning , 2015, Comput. Aided Geom. Des..

[24]  Leonidas J. Guibas,et al.  ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.

[25]  Zhang Xiong,et al.  A powerful 3D model classification mechanism based on fusing multi-graph , 2015, Neurocomputing.

[26]  Leonidas J. Guibas,et al.  A scalable active framework for region annotation in 3D shape collections , 2016, ACM Trans. Graph..

[27]  Daphna Weinshall,et al.  Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels , 2016, ArXiv.

[28]  Hongyan Li,et al.  Accumulative categorization: Online 3D shape classification for progressive collections , 2017, Graph. Model..