1 / 15

Indoor/Outdoor Classification December 1, 2009 Liu, Cheng Yang, Hsiu -Han Han, Seung Yeob

Indoor/Outdoor Classification December 1, 2009 Liu, Cheng Yang, Hsiu -Han Han, Seung Yeob. Human’s brain is an excellent photo analysis tool and is good at handling high-level information, such as facial recognition. High-level information.

yahto
Download Presentation

Indoor/Outdoor Classification December 1, 2009 Liu, Cheng Yang, Hsiu -Han Han, Seung Yeob

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Indoor/Outdoor Classification December 1, 2009Liu, ChengYang, Hsiu-HanHan, SeungYeob

  2. Human’s brain is an excellent photo analysis tool and is good at handling high-level information, such as facial recognition. • High-level information A lot of high-level information to do the indoor/ outdoor classification: Brightness, Background, People, Ground, etc. Motivation:

  3. However, human’s brain is not quite convenient for mass data analysis. • will get tired after long run • very expensive In the meanwhile, computers are still indispensable for the analysis of mass data. But not quite efficient for the high-level information. Motivation (Cont.): When processing mass data, can we utilize the low-level information for classification?

  4. Method: • 119 indoor photos and 102 outdoor photos were collected.

  5. Can you tell the differences between these two photos? Low-level Information: The ‘average photo’ for indoor photos The ‘average photo’ for outdoor photos

  6. for Indoor photos for Outdoor photos Big Difference Low-level Information (Cont): Some Difference Almost the Same

  7. It is no good to use the whole color matrix to compute the means and stds. Sampling Method: low efficiency Photos have different sizes. So the sizes of the color matrix would be different.

  8. Uniformly sampling: sample size N = 10,000 Sampling Method (Cont):

  9. Sampling Method (Cont): A natural question: is the information indeed uniformly distributed on the photos?

  10. Sampling Method (Cont): Non-uniformly sampling: sample size N = 10,000

  11. Classification • Method • Logistic regression • SVM • Mixture Gaussian • Samples • Uniform sampling • Non-uniform sampling

  12. Classification(Cont.)

  13. Classification(Cont.)

  14. Future work • Improving sampling method • Sample points based on histogram (most frequent values). • Seeking effective features • We now use purely linear features. Try other feature mapping.

  15. Q&A Any questions? Thank you

More Related