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Indra
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How to combine two features HOG and LBP to get better accuracy?

Asked by Indra
on 15 Jun 2014
Latest activity Commented on by chinnurocks on 28 Jul 2016
Dear all,,
I am working on people detection with training datasets. I used two different different feature extraction, HOG and LBP. Each of them will extract 324 feature vector (HOG) and 59 feature vector (LBP) for each training image. I am currently using simple concatenation to combine bothe features and then the results will be feed into SVM classifier to be classified. However, seems like this method gives a domination to HOG.
I have tried to find out another method, which is using fusion features, yet I have no knowledge how to do the algorithm. I really need help from the expert who know about the fusion feature to combine two features.
Really thank you

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Hey bro instead of using uniform LBP, I have used LBP and got 9164 elements but unable to train these features to 'SVMtrain' as I am unable to store all 9164 elements in one row in an excel sheet. Need your suggestions.

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1 Answer

Answer by Nikolay S. on 16 Mar 2015

Hi there. I've worked on a project where we have combined multiple features to achieve best detection. HOG and LBP were some of the used features BTW :). What you've been doing so far is a good point to start. You can try PCA on top fo that to reduce irrelevant features, preserving most dominant ones. Voting can also be nice- you apply an SVM to each feature vector, and the you have to combine their decisions- you can have "and"/"or"/"majority vote" schemes, to decide true (detection) or false (no detection). All above are relatively easy methods, with good potential to succeed. Another thing is use Cascade & Boosting- as Viola & Jones did. This is a powerful tool that will reject features in ascending order. But you will need to invest effort to get into it. Best regards, Nikolay

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