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Training and Evaluating of Object Bank Models

This presentation delves into dataset configuration, model training, evaluation, and conclusion of deformable part models and object bank analysis.

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Training and Evaluating of Object Bank Models

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  1. Training and Evaluating of Object Bank Models Presenter： Changyu Liu Advisor： Prof. Alex Interest ： Multimedia Analysis May 16th, 2013

  2. Contents Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan

  3. Dataset Setting--- Object Lists In this experiment, we firstly choose 10 objects, as: Table 1 Selected 10 Objects

  4. Dataset Setting--- Sample Configuration • Then, choose 961 total image(about 100 for each object) for training, 958 total image for evaluation, and 1331 total image for testing. • All these images are divided by 1:4 for positive and negative samples and are all from Image Net (http://www.image-net.org/) with most of them having a bounding box annotation.

  5. Dataset Setting--- Sample Configuration 3. We use these images to substitute VOC 2008 dataset and have generated as well as evaluated four deformable part models (other six models are on the way).

  6. Contents Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan

  7. Model Training---Overview In order to use Object Bank features, object models should be trained firstly. Here we introduced a Deformable Part Model(Felzenszwalb, CVPR 2008) for such training.The current adopted version was voc-release 3.l.

  8. Model Training--- Deformable Part (a) person detection Example (b1) coarse template (b2)part templates (b3) spatial model Fig. 1Deformable Part Model The deformable model include both a coarse global template and higher resolution part templates. The templates represent histogram of gradient features

  9. Model Training--- Results On average, it generated 1.5 models each day on the CQ-serials desktop. After training, we got 9 .mat model file, as:balloon_final.matsnail_final.matcandle_final.matsoccer ball_final.matlaptop_final.matairplane_final.matcar_final.matboat_final.matcow_final.mat

  10. Contents Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan

  11. Model Evaluation--- Deformable Part Then, we had a evaluation of each object on the selected 958 images, and got the Average Precision distribution map, as:

  12. Model Evaluation--- Deformable Part Fig. 2 AP of Airplane In which AP is average precision, Bbox 1 is bounding box from root placements, and Bbox 2 is bounding box from using predictor function.

  13. Model Evaluation--- Deformable Part Fig. 3 AP of Balloon

  14. Model Evaluation--- Deformable Part Last, we got 9 objects average precision, as: Table 2Average precision of nine objects Then, got 9 google images(1 image for each object for a bounding box test.

  15. Model Evaluation--- Deformable Part Fig. 4Balloon

  16. Model Evaluation--- Deformable Part Fig. 5Candle

  17. Model Evaluation--- Deformable Part Fig. 6Cow

  18. Model Evaluation--- Deformable Part Fig. 7Laptop

  19. Model Evaluation--- Deformable Part Fig. 8Soccer ball

  20. Contents Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan

  21. Model Evaluation--- Object Bank The second evaluation was tested on Object Bank. Table 3 Correlation Coefficient

  22. Model Evaluation--- Object Bank

  23. Contents Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan

  24. Conclusion Conclusion, 1)The width or height of selected image must >= 4 HOG bin(4*8 pixels). 2)It is feasible to use v3.1(not v5) code to generate object models for getting Object Bank features, and it took 1/1.5 day to get one model. The plan for next steps is,1) Move these codes to PSC for a further test in order to improve the process speed.2) Find what the needed 1000 objects names are.3) Choose and Make the dataset from Image Net.

  25. Reference [1] P. Felzenszwalb, D. McAllester, 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. McAllester, 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.

  26. Thank you!

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