In Defense of Nearest-Neighbor Based Image Classification

Mar 31, 2014

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Outline. Introduction. What degrades NN-image classification?. The Approximation Algorithm Using NN. Results and Experiments. Introduction. The problem of image classification has drawn considerable attention in the Computer Vision community.. Image classification methods can be roughly divided int

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In Defense of Nearest-Neighbor Based Image Classification

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A Fast and Scalable Nearest Neighbor Based Classification

A Fast and Scalable Nearest Neighbor Based Classification. Taufik Abidin and William Perrizo Department of Computer Science North Dakota State University. Outline. Nearest Neighbors Classification Problems

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Classification Methods: k-Nearest Neighbor Naïve Bayes

0. Classification Methods: k-Nearest Neighbor Naïve Bayes. Ram Akella Lecture 4 February 9, 2011 UC Berkeley Silicon Valley Center/SC. 0. Overview. Example The Naïve rule Two data-driven methods (no model) K-nearest neighbors Naïve Bayes. Example: Personal Loan Offer.

1.08k views • 57 slides

Nearest Neighbor Classifiers

Nearest Neighbor Classifiers. other names: instance-based learning case-based learning (CBL) non-parametric learning model-free learning. 1-NN. save all training data to classify a test example, compute distance to each training example Euclidean distance metric

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Nearest Neighbor

Nearest Neighbor. Ling 572 Advanced Statistical Methods in NLP January 12, 2012. Roadmap. Instance-based learning Examples and Motivation Basic algorithm: k-Nearest Neighbors Issues: Selecting Weighting Voting Summary HW #2. Instance-based Learning. aka “Lazy Learning”

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Nearest neighbor matching

Nearest neighbor matching. USING THE GREEDY MATCH MACRO. Note: Much of the code originally was written by Lori Parsons http://www2.sas.com/proceedings/sugi26/p214-26.pdf This code has been written with simplicity as a primary concern.

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K Nearest Neighbor Classification Methods

K Nearest Neighbor Classification Methods. Qiang Yang. Training Set. Used for prediction/classification Given input x, (e.g., <sunny, normal, ..?> #neighbors = K (e.g., k=3) Often a parameter to be determined The form of the distance function

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Nearest-Neighbor Classifiers

Nearest-Neighbor Classifiers. Requires three things The set of stored records Distance Metric to compute distance between records The value of k , the number of nearest neighbors to retrieve To classify an unknown record: Compute distance to other training records

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Classification Nearest Neighbor

Classification Nearest Neighbor. Instance based classifiers. Store the training samples Use training samples to predict the class label of unseen samples. Instance based classifiers. Examples: Rote learner memorize entire training data

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Regularized Double Nearest Neighbor Feature Extraction for Hyperspectral Image Classification

Regularized Double Nearest Neighbor Feature Extraction for Hyperspectral Image Classification. Hsiao-Yun Huang Department of Statistics and Information Science, Fu-Jen University. Hyperspectral Image Introduction 1. (image credit: AFRL). Hyperspectral Image Introduction 2.

799 views • 53 slides

Nearest Neighbor

Nearest Neighbor. Paul Hsiung March 16, 2004. Quick Review of NN. Set of points P Query point q Distance metric d Find p in P such that d(p,q) < d(p’,q) for all p’ in P. p. q. NN Used In…. Image databases [Pentland et al ] Color indexing [swain et al ]

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K Nearest Neighbor Classification Methods

K Nearest Neighbor Classification Methods. Qiang Yang. The K-Nearest Neighbor Method. Used for prediction/classification Given input x, (e.g., <sunny, normal, ..?> #neighbors = K (e.g., k=3) Often a parameter to be determined The form of the distance function

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A k -Nearest Neighbor Based Algorithm for Multi-Label Classification

http://lamda.nju.edu.cn. A k -Nearest Neighbor Based Algorithm for Multi-Label Classification. Min-Ling Zhang zhangml@lamda.nju.edu.cn. Zhi-Hua Zhou zhouzh@nju.edu.cn. National Laboratory for Novel Software Technology Nanjing University, Nanjing, China July 26, 2005. Outline.

559 views • 19 slides

K Nearest Neighbor Classification Methods

K Nearest Neighbor Classification Methods. Training Set. Used for prediction/classification Given input x, (e.g., <sunny, normal, ..?> #neighbors = K (e.g., k=3) Often a parameter to be determined The form of the distance function K neighbors in training data to the input data x:

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K-Nearest Neighbor

K-Nearest Neighbor. Different Learning Methods. Eager Learning Explicit description of target function on the whole training set Instance-based Learning Learning=storing all training instances Classification=assigning target function to a new instance Referred to as “Lazy” learning.

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Fast and Scalable Nearest Neighbor Based Classification

Fast and Scalable Nearest Neighbor Based Classification. Taufik Abidin and William Perrizo Department of Computer Science North Dakota State University. Unclassified Object. Search for the k-Nearest Neighbors. Vote the class. Training Set. Classification.

166 views • 16 slides

Classification Nearest Neighbor

Classification Nearest Neighbor. Instance based classifiers. Store the training samples Use training samples to predict the class label of test samples. Instance based classifiers. Examples: Rote learner memorize entire training data

217 views • 21 slides

Learning: Nearest Neighbor

Learning: Nearest Neighbor. Artificial Intelligence CMSC 25000 January 31, 2002. Agenda. Machine learning: Introduction Nearest neighbor techniques Applications: Robotic motion, Credit rating Efficient implementations: k-d trees, parallelism Extensions: K-nearest neighbor Limitations:

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Nearest Neighbor Classifier

Artificial Intelligence. Nearest Neighbor Classifier. Dae-Won Kim. School of Computer Science & Engineering Chung-Ang University. In last class, we learned bayesian classification approaches. However, we see some limitations.

468 views • 45 slides

Classification Nearest Neighbor

217 views • 19 slides

A Fast and Scalable Nearest Neighbor Based Classification

A Fast and Scalable Nearest Neighbor Based Classification. Taufik Abidin and William Perrizo Department of Computer Science North Dakota State University. Unclassified Object. Search for the k-Nearest Neighbors. Vote the class. Training Set. Classification.

236 views • 23 slides

Parameter Optimized Vertical, Nearest Neighbor- Vote and Boundary Based Classification

Parameter Optimized Vertical, Nearest Neighbor- Vote and Boundary Based Classification. Amal Perera, William Perrizo {amal.perera, william.perrizo}@ndsu.edu Dept. of CS, North Dakota State University. CATA 2007 – Honolulu, Hawaii. Outline. Introduction Background Our Approach

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Parameter Optimized Vertical, Nearest Neighbor- Vote and Boundary Based Classification

Parameter Optimized Vertical, Nearest Neighbor- Vote and Boundary Based Classification. Outline Introduction Background Our Approach Experimental Results Conclusions. Introduction. Computer Aided Detection (CAD): Interesting data mining applications Typical Medical Image Data Sets are

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