3/23/21 CSE 473/573 Introduction to Computer Vision and Image Processing ‘- SEGMENTATION QUESTIONS REGARDING ANYTHING? Slides: James Hays Isabelle Guyon, Erik Sudderth, Mark Johnson, Derek Hoiem ‘- 1 3/23/21 Image Segmentation • Segmentation attempts to partition the pixels of an image into groups that strongly correlate with the objects in an image • Typically the […]
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CS699 Lecture 10 Clustering What is Cluster Analysis? Cluster: A collection of data objects similar (or related) to one another within the same group dissimilar (or unrelated) to the objects in other groups Cluster analysis (or clustering, data segmentation, …) Finding similarities between data according to the characteristics found in
CS699 Lecture 2 Data Exploration Types of Data Sets – Data matrix, e.g., numerical matrix, crosstabs • Record – Relational records – Document data: text documents: term‐ frequency vector – Transaction data • Graph and network – World Wide Web – Social or information networks – Molecular Structures • Ordered – Video data: sequence of
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CS699 Lecture 6 Performance Evaluation Model Evaluation and Selection Evaluation metrics: How can we measure accuracy? Other metrics to consider? Use an independent test dataset instead of training dataset when assessing accuracy Methods for estimating a classifier’s accuracy: Holdout method, random subsampling Cross‐validation Bootstrap Comparing classifiers: Confidence
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2/16/2021 CSE 473/573 Introduction to Computer Vision and Image Processing ‘- FEATURE EXTRACTION ‘- 1 2/16/2021 Filters for features • Previously, thinking of filtering as a way to remove or reduce noise • Now, consider how filters will allow us to abstract higher-level ‘- “features”. • Map raw pixels to an intermediate representation that will
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4/1/2021 CSE 473/573 ‘- Introduction to Computer Vision and Image Processing 1 Spend 30 minutes writing up ideas of how the following may be solved. Think about how we moved from pixels to features to ???? ‘- • What other tools do we have in our tool bag that can now be applied to “objects”?
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4/6/2021 CSE 473/573 Introduction to Computer Vision and Image Processing ‘- Mobile Retriever: Use of context • Represent each document page as a “bag of visual words” • Build a reverse index of visual words from the lexicon ‘- • Perform ranked retrieval based on the number of “hits” for each document • Verify top
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2/18/2021 CSE 473/573 Introduction to Computer Vision and Image Processing ‘- PROJECT #1 ‘- 1 2/18/2021 Optical character recognition (OCR) ‘- Digit recognition, AT&T labs License plate readers http://www.research.att.com/~yann/ http://en.wikipedia.org/wiki/Automatic_number_plate_recognition Technology to convert scanned docs to text • If you have a scanner, it probably came with OCR software 3 Optical Character Recognition • Project
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CS699 Lecture 8 Association Rule Mining What Is Frequent Pattern Analysis? Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context of frequent itemsets and association rule mining Motivation:Findinginherentregularitiesindata What products were
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Assignment 8 Due: 4/1 Note: Show all your work. Problem 1 (20 points). Consider the following transactional database. (1) Mine all frequent itemsets using Apriori. Show all candidate itemsets and frequent itemsets. You should follow the process described in the book and lecture (i.e., C1 ¡ú L1 ¡ú C2 ¡ú L2 ¡ú …). Minimum support
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