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I am trying to implement image search based on paper "Scalable Recognition with a Vocabulary Tree". I am using SURF for extracting the features and key points. For example, for an image i'm getting say 300 key points and each key point has 128 descriptor values. My Question is how can I apply the K-Means Clustering algorithm on the data. I mean Do I need to apply clustering algorithm on all the points i.e., 300*128 values or Do I need to find the distance between the consecutive descriptor values and store the values and apply the clustering algorithm on that. I am confused and any help will be appreciated.

Thanks, Rocky.

Ilmari Karonen
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user731828
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3 Answers3

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From your question I would say you are quite confused. The vocabulary tree technique is grounded on the us of k-means hierarchical clustering and a TF-IDF weighting scheme for the leaf nodes.

In a nutshell the clustering algorithm employed for the vocabulary tree construction runs k-means once over all the d-dimensional data (d=128 for the case of SIFT) and then runs k-means again over each of the obtained clusters until some depth level. Hence the two main parameters for the vocabulary tree construction are the branching factor k and the tree depth L. Some improvements consider only the branching factor while the depth is automatically determined by cutting the tree to fulfill a minimum variance measure.

As for the implementation, cv::BOWTrainer from OpenCV is a good starting point though is not very well generalized for the case of a hierarchical BoW scheme since it imposes the centers to be stored in a simple cv::Mat while vocabulary tree is typically unbalanced and mapping it to a matrix in a level-wise fashion might not be efficient from the memory use point of view when the number of nodes is much lower than the theoretical number of nodes in a balanced tree with depth L and branching factor k, that is:

n << (1-k^L)/(1-k)

Andres Felipe
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For what I know I think that you have to store all the descriptors on a cv::Mat and then add this to a "Kmeans Trainer", thus you can finally apply the clustering algorithm. Here a snippet that can give you an idea about what I am talking: 

BOWKMeansTrainer bowtrainer(1000); //num clusters
bowtrainer.add(training_descriptors); // we add the descriptors
Mat vocabulary = bowtrainer.cluster(); // apply the clustering algorithm

And this maybe can be interesting to you: http://www.morethantechnical.com/2011/08/25/a-simple-object-classifier-with-bag-of-words-using-opencv-2-3-w-code/

Good luck!!

Str1101
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Checkout out the code in libvot, in src/vocab_tree/clustering.*, you can find a detailed implementation of the clustering algorithm.

Nothing More
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