Hey,
I dont't get it what to do exactly when computing the histogram. Do I have to compute and store a histogram for each training image or do I have to compute the histograms only for the two classes 'plane' and 'bike' which sums up all occurances of a codword over all images of a class. In the latter case do we need to normalize the histogram?
Thanks for your help!
compute histogram
Moderator: Computer Vision
Re: compute histogram
I just computed histogram-values for each image. with 120 images, i have an output-histogram 120x50. for example in histogram(20,10), you find the value how many feature vectors of image 20 were assigned into cluster 10. That's how I understood it and I really hope it's not totally wrong cause I am already getting insane with this homework.
Hope that helps
PS: Read the first sentence of Part 2.B. I guess that's where these histogram is needed for...
Hope that helps

PS: Read the first sentence of Part 2.B. I guess that's where these histogram is needed for...
Re: compute histogram
As is turns out there are two steps which involve computing a histogram. However, we are actually only concerned with one of them.
1) First, sift features are nothing else than histograms over the gradient discretized by a certain number of directions (this is already done by the given implementation).
2) Second, the features we are using for classification are NOT the sift features. Prof. Roth really put a lot of focus on this point. Again, we are NOT using sift features directly. Instead, we compute a codebook over all sift feature vectors (KMeans). Having finished that, we then compute the actual features as a histogram by finding the closest visual codeword in the codebook for each feature vector (corresponding to each interest point) in a given image. In other words: We count how often certain visual words occur in an image. The resulting histogram PER IMAGE serves as input to the classifiers (normalized in the case of the svm).
1) First, sift features are nothing else than histograms over the gradient discretized by a certain number of directions (this is already done by the given implementation).
2) Second, the features we are using for classification are NOT the sift features. Prof. Roth really put a lot of focus on this point. Again, we are NOT using sift features directly. Instead, we compute a codebook over all sift feature vectors (KMeans). Having finished that, we then compute the actual features as a histogram by finding the closest visual codeword in the codebook for each feature vector (corresponding to each interest point) in a given image. In other words: We count how often certain visual words occur in an image. The resulting histogram PER IMAGE serves as input to the classifiers (normalized in the case of the svm).
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