indoor/outdoor classification december 1, 2009 liu, cheng yang, hsiu -han han, seung yeob

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Indoor/Outdoor Classification

December 1, 2009

Liu, Cheng

Yang, Hsiu-Han

Han, Seung Yeob

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Motivation: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.

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Motivation (Cont.):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.

When processing mass data, can we utilize the low-level information for classification?

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Method:

853 1280

853 1280

853 1280

77 7

:

23 28

85 10

:

38 41

87 1

:

15 13

R

G

B

mean_R, std_R

mean_G, std_G

mean_B, std_B

119 indoor photos and 102 outdoor photos were collected.

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Low-level Information:Can you tell the differences between these two photos?

The ‘average photo’ for indoor photos

The ‘average photo’ for outdoor photos

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Low-level Information (Cont):for Indoor photos for Outdoor photos

Big Difference

Some Difference

Almost the Same

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Sampling Method:It is no good to use the whole color matrix to compute the means and stds.

1. low efficiency2. Photos have different sizes. So the sizes of the color matrix

would be different.

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

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

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

A natural question: is the information indeed uniformly distributed on the photos?

Sampling Method (Cont):

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

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

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Classification• Method

– Logistic regression– SVM– Mixture Gaussian

• Samples– Uniform sampling– Non-uniform sampling

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Classification(Cont.)

Error rate(%)

Uniform sampling Non-uniform sampling

Logistic regression 17.65% 14.48%

SVM 18.55% 16.29%

Mixture Gaussian 39.37% 37.10%

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Classification(Cont.)

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

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.

12/1/2009 Liu, Cheng Yang, Hsiu-Han Han, Seung Yeob

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CS 590 Project

Q&A

Any questions?

Thank you