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HOGgles Visualizing  Object Detection Features C.  Vondrick HOGgles Visualizing  Object Detection Features C.  Vondrick

HOGgles Visualizing Object Detection Features C. Vondrick - PowerPoint Presentation

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HOGgles Visualizing Object Detection Features C. Vondrick - PPT Presentation

HOGgles Visualizing Object Detection Features C Vondrick A Khosla T Malisiewicz A Torralba ICCV 2013 presented by Ezgi Mercan Object Detection Failures Why do our detectors think water looks like a car ID: 762242

cse590v 2013 image hog 2013 cse590v hog image descriptor detection paired learning object basis dictionary car feature original reconstructing

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HOGglesVisualizing Object Detection Features C. Vondrick , A. Khosla , T. Malisiewicz , A. Torralba ICCV , 2013 . presented by Ezgi Mercan

Object Detection Failures Why do our detectors think water looks like a car? D ata set?Machine learning method?Features? 10/11/2013 CSE590V 2 A high scoring car detection from DPM.

Object Detection Failures 10/11/2013 CSE590V 3 Object Categories Aeroplane Table Bicycle Dog Bird Horse Boat Motorbike Bottle Person Bus Potted plantCarSheepCatSofaChairTrainCowTV/ monitor Person Chair Car

Reconstructing an Image 10/11/2013 CSE590V 4 original image copied patches blending interpolation Reconstructing an image from its local descriptors, Philippe Weinzaepfel , Hervé Jégou and Patrick Pérez, Proc. IEEE CVPR’11. calculate SIFT elliptic region of interestaffine normalization to square patchlocal descriptor

Histogram of Oriented Gradients 10/11/2013 CSE590V 5 Histograms of oriented gradients for human detection, Dalal, N . , Triggs , B., CVPR’05. original image R-HOG descriptor (HOG glyph) R-HOG descriptor weighted by positive SVM weights R-HOG descriptor weighted by negative SVM weights

Algorithms10/11/2013 CSE590V 6 Image: HOG descriptor: HOG inverse: Exemplar LDA Ridge Regression  

Algorithms Direct Optimization Image: HOG descriptor: Image basis: Coefficients:   10/11/2013 CSE590V 7

Algorithms Paired Dictionary Learning Image: HOG descriptor: Image basis: HOG basis: Coefficients:   10/11/2013 CSE590V 8

Paired Dictionary Learning 10/11/2013 CSE590V 9 HOG feature   HOG Basis   Image Basis   HOG Inversion  

Paired Dictionary Learning10/11/2013 CSE590V 10 Solving paired dictionary learning problem: s.t. ,   some learned pairs of dictionaries for and  

Feature Visualization10/11/2013 CSE590V 11 Origina l ELDA Ridge Regression Direct Optimization Paired Dict. Learning

10/11/2013 CSE590V 12

10/11/2013 CSE590V 13

Deformable Parts Model 10/11/2013 CSE590V 14 Potted Plant Chair Does HOG capture color? Object detection with discriminatively trained part-based models, P . Felzenszwalb , R. Girshick , D. McAllester , and D. Ramanan . PAMI, 2010.

Computers can see better than us10/11/2013 CSE590V 15

Questions?10/11/2013 CSE590V 16 Visit web.mit.edu/ vondrick /ihog / for more cool stuff.