Image Classification Practical [MATLAB-based project]

http://www.robots.ox.ac.uk/~vgg/share/practical-image-classification.htm

First submitted by Andrea Vedaldi on 14 Sep 2011

Updated by ankit arya on 26 Aug 2012

Tutorial on semantic image classification with bag-of-words representations and SVM classifiers

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Descriptions and Ratings (3)

Date	Contributor	Description	Rating
26 Aug 2012	ankit arya		5
5 Oct 2011	MathWorks Classroom Resources Team	Course taught by Andrea Vedaldi and Andrew Zisserman in the Robotics Research Group, Department of Engineering Science, University of Oxford. The material includes MATLAB code and related data files.
14 Sep 2011	Andrea Vedaldi	In image classification, an image is classified according to its visual content. For example, does it contain an airplane or not. An important application is image retrieval - searching through an image dataset to obtain (or retrieve) those images with particular visual content. The goal of this practical is to get basic practical experience with image classification. It includes: (i) training a visual classifier for five different image classes (aeroplanes, motorbikes, people, horses and cars) (ii) assessing the performance of the classifier by computing a precision-recall curve (iii) varying the visual representation used for the feature vector, and the feature map used for the classifier and (iv) obtaining training data for new classifiers using Bing image search. By the Oxford Visual Geometry Group, based on the computer vision library vlfeat.org.

26 Aug 2012

ankit arya

5 Oct 2011

MathWorks Classroom Resources Team

Course taught by Andrea Vedaldi and Andrew Zisserman in the Robotics Research Group, Department of Engineering Science, University of Oxford.

The material includes MATLAB code and related data files.

14 Sep 2011

Andrea Vedaldi

In image classification, an image is classified according to its visual content. For example, does it contain an airplane or not. An important application is image retrieval - searching through an image dataset to obtain (or retrieve) those images with particular visual content.
The goal of this practical is to get basic practical experience with image classification. It includes: (i) training a visual classifier for five different image classes (aeroplanes, motorbikes, people, horses and cars) (ii) assessing the performance of the classifier by computing a precision-recall curve (iii) varying the visual representation used for the feature vector, and the feature map used for the classifier and (iv) obtaining training data for new classifiers using Bing image search.
By the Oxford Visual Geometry Group, based on the computer vision library vlfeat.org.

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Tag Activity

Tag	Applied By	Date/Time
academic	Akin	12 Jun 2013 at 7:44am
computer vision	Rashmi T	23 Apr 2013 at 4:44am
computer science	Rashmi T	23 Apr 2013 at 4:44am
image classification	Rashmi T	23 Apr 2013 at 4:43am
learning	Rashmi T	23 Apr 2013 at 4:43am
language english	Rashmi T	23 Apr 2013 at 4:43am
tutorial	Rashmi T	23 Apr 2013 at 4:43am
image and video processing	Rashmi T	23 Apr 2013 at 4:42am
academic	chintan Shah	6 Jan 2013 at 10:29am
image classification by fractal	Bouzid Bouhaddouza	7 Dec 2012 at 12:37pm
academic	Raj	10 Jul 2012 at 6:44am
academic	vrsec vijayawada	22 Dec 2011 at 1:25am
language english	MathWorks Classroom Resources Team	5 Oct 2011 at 9:19am
image and video processing	MathWorks Classroom Resources Team	5 Oct 2011 at 9:19am
tutorial	MathWorks Classroom Resources Team	5 Oct 2011 at 9:19am
country gb	MathWorks Classroom Resources Team	5 Oct 2011 at 9:19am
computer science	MathWorks Classroom Resources Team	5 Oct 2011 at 9:19am
academic	MathWorks Classroom Resources Team	5 Oct 2011 at 9:19am
learning	Andrea Vedaldi	14 Sep 2011 at 2:40pm
visual features	Andrea Vedaldi	14 Sep 2011 at 2:40pm
academic	Andrea Vedaldi	14 Sep 2011 at 2:40pm
image classification	Andrea Vedaldi	14 Sep 2011 at 2:40pm
computer vision	Andrea Vedaldi	14 Sep 2011 at 2:40pm
country gb	Andrea Vedaldi	14 Sep 2011 at 2:40pm
language english	Andrea Vedaldi	14 Sep 2011 at 2:40pm
tutorial	Andrea Vedaldi	14 Sep 2011 at 2:40pm
computer science	Andrea Vedaldi	14 Sep 2011 at 2:40pm
image and video processing	Andrea Vedaldi	14 Sep 2011 at 2:40pm