Experiment 4

Clustering by EM and K-mean

In experiment 4, we cluster the images and learn a template for each cluster. We use both the EM algorithm and the K-mean algorithm for clustering.

Negative experience in Experiment 4. When the object shapes of different categories are not very different, our method often fails to distinguish them if we start from random clustering. The above difficulty is not caused by the model or the EM or K-mean iteration, but mainly by the fact that random clustering gives poor initialization. The local learning in Experiment 9 can be used to address this problem.

(1) data, codes, and readme for EM clustering based on active basis model (March 2009)
(1.1) Code that pools q() from 2 large natural images. (May 2009)

Experiment 4.1. EM algorithm: Learned templates from the mixture of the three sets of positive training images in Experiment 3. Image size is 120*150. Number of elements in each template is 40. Number of iteration is 4. eps1 eps2 eps3


Experiment 4.1. EM cluster 1 eps


Experiment 4.1. EM cluster 2 eps


Experiment 4.1. EM cluster 3 eps
(1.2) Code with local normalization (September 2009)

Experiment 4.1. Local normalization of filter responses with a window whose half size is 20x20.
eps1 eps2 eps3
(2) data, codes, and readme for K mean clustering based on active correlation

Experiment 4.1. Templates of three clusters learned by active correlation K mean. eps1 eps2 eps3
(2.1) Code with local normalization

Experiment 4.1. Templates of three clusters learned by active correlation K mean. eps1 eps2 eps3
(3) clustering horses at different directions by EM (March 2009)
(3.1) Code that pools q() from 2 large natural images. (May 2009)

Experiment 4.2. EM algorithm: Learned templates for 57 images of horses facing two different directions. Image size is 120*150. Number of elements in each template is 50. Number of iterations is 4. eps1 eps2


Experiment 4.2. EM cluster 1 eps


Experiment 4.2. EM cluster 2 eps
(3.2) Code with local normalization (September 2009)

Experiment 4.2. Number of elements in each template is 40. Number of iterations is 4. eps1 eps2
(4) clustering horses at different directions by K mean

Experiment 4.2. Templates of two clusters learned by active correlation K mean. Number of iterations is 8. eps1 eps2
(5) clustering out head-shoulders from negatives by EM (March 2009)
(5.1) Code that pools q() from 2 large natural images. (May 2009)

Experiment 4.3. Cluster 1. One positive is misplaced into cluster 2. eps

(6) clustering out head-shoulders from negatives by K mean

Experiment 4.3. Cluster 1. No positive is misplaced into cluster 2. eps

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