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REU: Week Two

In the second week of the REU program, we focus on clustering techniques vital for grouping data, particularly in image analysis. Using K-means clustering, we assess pixel relationships based on color intensity and location. Following that, we construct Bag of Words representations at intervals of 13 pixels, resulting in histograms that illustrate image characteristics. These histograms undergo logistic regression for weight optimization, enhancing detection capabilities. Additionally, we explore anomalous behavioral tracking in videos and scene recognition in movies, refining our approach to object detection.

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REU: Week Two

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  1. Clustering, Bag of Words, and Image Detection REU: Week Two

  2. Clustering • Useful for grouping data • Related pixels in an image • Done based on color intensity and location • Utilized K-means

  3. Clustering CoLORoNLY Color And weighted Location

  4. Generating the Codebook • Generate Feature Descriptors as we did last week. • Generated by applying multiple Gaussian derivatives at random points in the images. • Then cluster using k-means.

  5. Bag of Words • Next generate the bag of words representations of images at set intervals. • I chose every spacing of 13 pixels. • From here find it’s the clustered center these words are closest to for the image and this will give us a histogram.

  6. Histograms

  7. Image Detection • Then run the histograms through logistic regression to get the proper weights. • Following this get more feature data from images and apply the weights and squashing function. • Then apply a threshold to determine detections.

  8. Confusion Matrix *threshold of .5

  9. Project Thoughts • Anomalous behavior in video. • Tracking of objects through occlusions based on predicted trajectory. • TRECVID • Scene Recognition in Movies

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