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Patch-Based Image Classification Using Image Epitomes

David Andrzejewski Computer Sciences 766 Fall 2005. Patch-Based Image Classification Using Image Epitomes. Given Positive and negative example images for a certain classification (contains face, is outdoors, etc) Do

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Patch-Based Image Classification Using Image Epitomes

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  1. David Andrzejewski Computer Sciences 766 Fall 2005 Patch-Based Image Classification Using Image Epitomes

  2. Given Positive and negative example images for a certain classification (contains face, is outdoors, etc) Do Develop classifier capable of classifying new images as positive or negative Problem Statement

  3. Consists of patches and mappings Patches and mappings are learned with EM Applications in vision (de-noising,segmentation,others) Image Epitomes Image epitome A set of image patches Input image www.research.microsoft.com/~jojic/epitome.htm

  4. Original image can then be reconstructed by mosaicing epitome patches Image Reconstruction www.research.microsoft.com/~jojic/epitome.htm

  5. Recognition / Detection / Classification The smiling point Epitome of 295 face images Images with the highest total posterior at the “smiling point” Images with the lowest total posterior at the “smiling point” www.research.microsoft.com/~jojic/epitome.htm

  6. Construct collage of positive and negative examples Learn the image epitome of the training collage Find epitome patches that are preferentially mapped into the positive example images in the collage Calculate P(patch(i)|pos/neg) for these patches (also use psuedo-counts) Use these patches to classify new images by calculating odds ratio Approach

  7. Preliminary Results Training Collage Epitome Negative Test Images Positive Test Images

  8. Difficult to incorporate new examples Would need to add to collage and re-learn epitome (is there a better way?) “Bag of words” → Spatial information discarded Not model-based Pose/Illumination/Scale-variant Only way to handle variation is to include training examples for various conditions Problems with Approach

  9. Cluster training images Ex: Training images w/ low vs high illumination Discriminative patches may map exclusively to one subset of positive images → take this into account Change “winner take all” for P calculations Consider relative probabilities of 'near matches' Account for multiple mappings somehow Potential Modifications

  10. V. Cheung, B. J. Frey, and N. Jojic, Video epitomes, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005 N. Jojic, B. J. Frey, and A. Kannan, Epitomic analysis of appearance and shape, Proc. 9th Int. Conf. Computer Vision, 2003 R. Fergus, P. Perona, A. Zisserman, Object Class Recognition by Unsupervised Scale-Invariant Learning, Proc. of the IEEE Conf on Computer Vision and Pattern Recognition, 2003 Testing images from Google Images and Flickr References

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