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Clustering with Application to Fast Object Search

Clustering with Application to Fast Object Search

03/08/12. Clustering with Application to Fast Object Search. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. This section. Clustering: grouping together similar points, images, feature vectors, etc. Segmentation: dividing the image into meaningful regions

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898 views • 74 slides
AMCS/CS 340: Data Mining

AMCS/CS 340: Data Mining

Clustering. AMCS/CS 340: Data Mining. Xiangliang Zhang King Abdullah University of Science and Technology. Grouping fruits. Grouping apple with apple , orange with orange and banana with banana. 2. Give pictures to a computer. 3. C hange pictures to data. 4.

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793 views • 49 slides
CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision. Pattern Recognition. Computer vision: related topics. Pattern recognition. One of the leading vision conference: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pattern recognition and machine learning

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654 views • 42 slides
Recap: Multiple Views and Motion

Recap: Multiple Views and Motion

Recap: Multiple Views and Motion. Epipolar geometry Relates cameras in two positions Fundamental matrix maps from a point in one image to a line (its epipolar line) in the other Can solve for F given corresponding points (e.g., interest points) Stereo depth estimation

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509 views • 42 slides
CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision

CSE 185 Introduction to Computer Vision. Pattern Recognition. Computer vision: related topics. Pattern recognition. One of the leading vision conference: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pattern recognition and machine learning

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440 views • 42 slides
Image Segmentation

Image Segmentation

Image Segmentation. What is Image Segmentation?. Segmentation: Split or separate an image into regions To facilitate recognition, understanding, and region of interests (ROI) processing Ill-defined problem The definition of a region is context-dependent. Outline. Discontinuity Detection

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1.61k views • 32 slides
PROBLEM: Identifying customer similarities

PROBLEM: Identifying customer similarities

?. PROBLEM: Identifying customer similarities. How do you take a log of transactional data from your customers (or audience, users, subscribers, citizens, etc.) and use it to understand them.

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440 views • 28 slides
Local Clustering Algorithm

Local Clustering Algorithm

Local Clustering Algorithm. DISCOVIR 9-16-2002. Current Situation. Image collection within a client is modeled as a single cluster. Proposed Improvement. Multiple clusters exist in the image collection. Group of similar local cluster A. Group of similar local cluster B.

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343 views • 20 slides
Local Clustering Algorithm

Local Clustering Algorithm

Local Clustering Algorithm. DISCOVIR 9-16-2002. Current Situation. Image collection within a client is modeled as a single cluster. Proposed Improvement. Multiple clusters exist in the image collection. Group of similar local cluster A. Group of similar local cluster B.

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213 views • 20 slides
K-means

K-means

K-means. Arbitrarily choose k objects as the initial cluster centers Until no change, do (Re)assign each object to the cluster to which the object is the most similar, based on the mean value of the objects in the cluster

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112 views • 5 slides

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