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B.U. Solid State Laboratory, May 2013 - July 2014
B.U. Solid State Laboratory, May 2013 - July 2014
B.U. Solid State Laboratory, May 2013 - July 2014
Spatial Pyramid Matching for Scene Classification
CMU16-720, Computer Vision
September 2020
Abstract:
Given a set of images, the goal was to determine the location of the scenes using Spatial Pyramid Matching. This representation is based off of the bag of visual words approach.
Process:
I started by applying a filter bank to each our images to tease out the high frequency signals. The filters consisted of variations of the Gaussian and Gaussian-Laplace filters. From here, I took samples of pixels from the filter responses and passed it to a K-mean clustering algorithm to generate a 'visual words' dictionary. This was done over thousands of training images to generate a model where I could create word maps describing each scene image. Finally I developed a recognition model using the spatial pyramid matching technique which mapped each test image to the closest historgram describing the image.
Results:
To the right are some examples of the word maps generated using this model. I was able to achieve an accuracy of 65.5% using this image classification model.
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