Training and Evaluating of Training and Evaluating of Object Bank ModelsObject Bank Models
Presenter : Changyu LiuAdvisor : Prof. AlexInterest : Multimedia Analysis
May 16th, 2013
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Contents
Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan
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Dataset Setting--- Object Lists
OB ID Object Name WNID
10477 knife n029739041253 balloon n02782093
12498 snail n01944390
11515 candle n02948072
1176 soccer ball n04254680
1190 laptop n03642806
1232 airplane n02690373
12982 car n02701002
1329 boat n03329663
1103 cow n01887787
In this experiment, we firstly choose 10 objects, as:
Table 1 Selected 10 Objects
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Dataset Setting--- Sample Configuration
1. Then, choose 961 total image(about 100 for each object) for training, 958 total image for evaluation, and 1331 total image for testing.
2. 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.
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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).
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Contents
Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan
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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.
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Fig. 1 Deformable Part Model
Model Training--- Deformable Part
The deformable model include both a coarse global template and higher resolution part templates. The templates represent histogram of gradient features
(b1) coarse template (b2)part templates (b3) spatial model (a) person detection Example
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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
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Contents
Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan
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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:
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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.
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Model Evaluation--- Deformable Part
Last, we got 9 objects average precision, as:
Object AP of Bbox1 AP of Bbox2balloon 0.428 0.439
snail 0.184 0.201
candle 0.203 0.196
soccer ball 0.376 0.376
laptop 0.472 0.479
airplane 0.644 0.652
car 0.518 0.526
boat 0.495 0.488
cow 0.416 0.405
Table 2 Average precision of nine objects
Then, got 9 google images(1 image for each object for a bounding box test.
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Contents
Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan
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Model Evaluation--- Object Bank
Object Correlation Coefficient
balloon 0.71806
snail 0.86498
candle 0.85893
soccer ball 0.84165
laptop 0.73821
airplane 0.79783
car 0.48926
boat 0.75255
cow 0.71712
Table 3 Correlation Coefficient
The second evaluation was tested on Object Bank.
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Contents
Dataset Setting Model Training Model Evaluation in Deformable Part Model Evaluation in Object Bank Conclusion and Plan
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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.
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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.