1. + Speed Up Texture Classification in Clothing Retrieval System 電機三 吳瑋凌

2. + What is clothing retrieval system? When a clothing image comes, we’d like to find the same pictures or the similar ones.

3. + What’s the data in images? HOG LBP Color Histogram Color Moment Skin

4. + Motivation Large scale computing efficiency => want to find a way to speed up the system

5. + Method Find some conditions to classify images Only need to compute features with those in the same class

6. + Experiment Use HOG to classify different textures Define 5 groups and use 25 training pictures for each

7. + HOG(Histogram of oriented gradients ) Detect the edge of items Judge clothing texture 3*3*9=81 dimensions z cell block

8. + How to find conditions threshold Find a threshold making the precision high enough after splitting

9. + How to find conditions Don’t define threshold Threshold=(max1+min2)/2

10. + Experiment One dimension  at most 10 threshold values Find the one having the max precision in all dimensions Set Paim <threshold>threshold

11. + Classtree Paim=0.7

12. + Results 5 pictures for each group(total=25 pictures) Calculate precision and recall for each group

13. + Discussion The relationship of P,R and Paim The higher Paim, the higher P and R Paim

14. + Discussion Use F1 to evaluate F1=2*P*R/(P+R) Paim

15. + Discussion The relationship of training time and Paim The higher Paim, the longer training time

16. + Conclusion We can find some more important entry to classify some groups Then we can only compute features of query image with those in the same groups

17. + Thanks for your listening :)