1. HCP: A Flexible CNN Framework for Multi-Label Image Classification
Source :  IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, 38(9):
Authors : Wei Y, Wei X, Lin M, et al.
Speaker : Jiefan Tan
Date : 2017/6/29
國立台灣大學資訊工程學系

2. Outline Introduction Proposed method Experiment Conclusions
Hypotheses Extraction
Training HCP
Experiment
Conclusions

3. Introduction- single-label image classification
Hand-crafted features
classifier
Image classification
+10%
CNN

4. Introduction- single-label VS. Multi-label images
Roughly aligned
mis-aligned
occluded

5. Proposed method- infrastructure of the proposed HCP

6. Proposed method- Hypotheses Extraction
Source image
Bounding boxes-BING
Hypotheses-Bb
Hypotheses-HS(resize)
Filter out hypotheses:
1.Small area(<900 pixel)
2.High height/width (width/height)ratios(>4)
High object detection recall rate
Small number of hypotheses
High computational efficiency

7. Proposed method- Training HCP
pre-train (parameter initialization)
single-label image
image-fine-tuning(initialize final fully-connected)
multi-label image
hypotheses-fine-tuning
hypotheses
cross-hypothesis max-pooling
𝑣 (𝑗) =max⁡( 𝑣 1 𝑗 , 𝑣 2 𝑗 ,…, 𝑣 𝑚 𝑗 )
𝑣 𝑖 𝑖=1,…,𝑚 is the vector of output
j=1,…c is the 𝑗 𝑡ℎ component of 𝑣 𝑖
m is the number of images
c is the number of categories

8. Proposed method- Training HCP
cross-hypothesis max-pooling
squared loss
𝐽= 1 𝑁 𝑖=1 𝑁 𝑘=1 𝑐 ( 𝑝 𝑖𝑘 − 𝑝 𝑖𝑘 ) 2
𝑝 𝑖 is the ground-truth probability vector of the 𝑖 𝑡ℎ image
𝑝 𝑖 is the predictive probability vector
N is the number of images
c is the number of categories

9. Proposed method- HCP

10. Experiment Datasets Shared CNN VOC 2007 (trainval/test = 5011/4952)
Alex Net
VGG Net

11. Experiment Results
1.mAP-mean of Average Precision
2.Number-hypotheses

12. Experiment Results

13. Conclusions No ground-truth bounding box information is required.
Robust to noisy and/or redundant hypotheses.
Can be well pre-trained by a large single-label image dataset.
The HCP outputs are intrinsically multi-label prediction results.

14. Thank you!