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POneed advise on sift feature - is there such thing as a good feature?
 

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  1. POneed advise on sift feature - is there such thing as a good feature?
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    <p>I am trying out vlfeat, got huge amount of features from an image database, and I am testing with the ground truth for mean average precision (MAp). Overall, I got roughly 40%. I see that some of the papers got higher MAp, while using techniques very similar to mine; the standard bag of word.</p> <p>I am currently looking for an answer for obtaining higher MAp for the standard bag of word technique. While I see that there are other implementation such as SURF and what not, let's stick to the standard Lowe's SIFT and the standard bag of word in this question.</p> <p>So the thing is this, I see that vl_sift got thresholding to allow you to be more strict on feature selection. Currently, I understand that going for higher threshold might net you smaller and more meaningful "good" features list, and possibly reduce some noisy features. <strong>"Good" features mean, given the same images with different variation, very similar features are also detected on other images</strong>.</p> <p>However, how high should we go for this thresholding? Sometimes, I see that an image returns no features at all with higher threshold. At first, I was thinking of keep on adjusting the threshold, until I get better MAp. But again, I think it's a bad idea to keep on adjusting just to find the best MAp for the respective database. So my questions are:</p> <ol> <li><p>While adjusting threshold may decrease numbers of features, does increasing threshold always return a lesser number yet better features? </p></li> <li><p>Are there better approaches to obtain the good features?</p></li> <li><p>What are other factors that can increase the rate of obtaining good features?</p></li> </ol>
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    1. COIrrelevant of what parametric method X you are using, it is relatively common to adjust the parameters like you say. But, the approach to do so is different from using the whole dataset like that. What you do is separate your data in training and test, then you adjust the parameters for your training data, and check how they work out for the training data. There are multiple ways on how this separation between training and test must be done in order to get accurate results, but this is all the basics of machine learning. Are you doing this form of separation ? It is not mentioned in the post.
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