Deformable Part Model Presenter Liu Changyu Advisor Prof

Deformable Part Model Presenter： Liu Changyu Advisor： Prof. Alex Hauptmann Interest ： Multimedia Analysis April 11 st, 2013

Contents l l l Introduction Model Learning Experiment Conclusion CMU - Language Technologies Institute 2

Introduction 1. Research Question 1) Object bank is just a image representation for high-level visual tasks, it should be used combing with detailed efficient traning method. 2) For difficult tasks, such as extending Object Bank to over 1000 objects and benchmarks of the PASCAL Challenge, it need new traning method to improve the average precision. 3) So we want to combine use the part model that proposed at CVPR in 2008. CMU - Language Technologies Institute 3

Introduction 2. What’s Deformable Part Model? Deformable Part is a discriminatively trained, multiscale model for image training that aim at making possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose. CMU - Language Technologies Institute 4

Contents l l l Introduction Model Learning Experiment Conclusion CMU - Language Technologies Institute 5

Model--- Deformable Part (a) person detection Example (b 1) coarse template (b 2)part templates (b 3) spatial model Fig. 1 Deformable Part Model The deformable model include both a coarse global template covering an entire object and higher resolution part templates. The templates represent histogram of gradient features CMU - Language Technologies Institute 6

Model---Deformable Part Fig. 2 illustrates a placement of such a model in a HOG pyramid. The root filter location defines the detection window (the pixels inside the cells covered by the filter). The part filters are placed several levels down in the pyramid, so the HOG cells at that level have half the (a) Image pyramid (b)HOG feature pyramid size of cells in the root Fig. 2 Pyramids of Deformable Part Model filter level. CMU - Language Technologies Institute 7

Model---Deformable Parts The score of a placement is given by the scores of each filter (the data term) plus a score of the placement of each part relative to the root (the spatial term), is the w × h × 9 × 4 weight vector are the features in a w×h subwindow of a HOG pyramid. gives the location of the i-th part relative to the root location. ai and bi are two dimensional vectors coefficients for measuring a score for each possible placement of the i-th part. Where CMU - Language Technologies Institute 8

Contents l l l Introduction Model Learning Experiment Conclusion CMU - Language Technologies Institute 9

Learning Latent SVMs This model use Latent SVMs to have a classification. As: where is a vector of model parameters, needed to be learned first, according to: z is a set of latent values. CMU - Language Technologies Institute 10

Contents l l l Introduction Model Learning Experiment Conclusion CMU - Language Technologies Institute 11

Experiment CMU - Language Technologies Institute 12

Experiment We execute the matlab code as…. . CMU - Language Technologies Institute 13

Contents l l l Introduction Model Algorithm Experiment Conclusion CMU - Language Technologies Institute 14

Conclusion 1)Experiment has not completed yet, it needed more object models used for deformable part training. 2) Computation need to be speed up. CMU - Language Technologies Institute 15

Reference [1] P. Felzenszwalb, D. Mc. Allester, D. Ramanan. A Discriminatively Trained, Multiscale, Deformable Part Model. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008 [2] P. Felzenszwalb, R. Girshick, D. Mc. Allester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, Sep. 2010. [3] Level Image Representation for Scene Classification and Semantic Feature Sparsification. Proceedings of the Neural Information Processing Systems (NIPS), 2010. CMU - Language Technologies Institute 16

Thank you! CMU - Language Technologies Institute 17