VisiQ

Computational Salience Based Approach for Print Quality Assessment

The VisiQ is developing an unsupervised method to categorize an unknown set of images into seperate clusters.

Goals

The goal of the project is to develop a system that is able to categorize a set of unknown images. Images should be categorized based on objects that images consist of. The system must be unsupervised, hence it cannot have any prior information about the images, e.g. class labels, training images, etc.

News

26.4.2010

Randomized Caltech 101 image set published and added to the webpage. Please see the downloads section.

19.3.2009

Page updated. New links added to a image database and rival systems.

25.3.2009

Unsupervised VOC www-demo published. Try! Limited access.

2.8.2008

Website published.

Contact information

Lappeenranta University of Technology
Teemu Kinnunen		WWW	Doctoral student, Master of science
Joni Kämäräinen		WWW	Professor
Lasse Lensu		WWW	Associate Professor
Heikki Kälviäinen		WWW	Professor
Helsinki University of Technology
Mari Laine-Hernandez		not available	Doctoral student, Master of science
Pirkko Oittinen		not available	Professor

This project is part of the research in the Machine Vision and Pattern Recognition Research Group.

Downloads

• Randomized Caltech 101 dataset: RandomizedCaltech101.tar.gz [113MB]
• Randomized Caltech 101 Mask files: RandomizedCaltech101Masks.tar.gz [5.9MB]

Demo

Demo is based on the system that was developed for Kinnunen's Master's thesis Unsupervised visual object categorization, (2008).

WWW-DEMO (Limited access)

Links to other resources

Image search engines

• ImageCLEF Homepage

Databases and competitions

• Caltech 101 dataset: Homepage
• Image-Net: Homepage
• Pascal network: Homepage

Researchers

• Fei-Fei Li: Homepage
• David Lowe: Homepage
• Andrew Zisserman: Homepage
• Andrea Vedaldi: Homepage
• Tamara L. Berg: Homepage
• Charles Leek: Homepage (psychology)
• John E. Hummel: Homepage (psychology)
• Gert Kootstra: Homepage (Saliency detection, Active vision, SLAM, etc)

Image categorization systems

• PicSOM: Homepage
• Alipr: Homepage

Interest points (detectors and descriptors)

• Featurespace: Homepage
• Lip-vireo: Video retrival group (Hong Kong)
• Dorko's code: Homepage

Saliency

• Discriminant Saliency Detection: Statistical Visual Computing Lab, UCSD

Publications

1. Articles in international compilation works and in international scientific conference proceedings with referee practice
   1. Teemu Kinnunen, Joni-Kristian Kämäräinen, Lasse Lensu, Heikki Kälviäinen, International Workshop on Self-Organizing Maps (WSOM 2009), Bag-of-Features Codebook Generation by Self-Organisation, (2009) pdf (3.16 MB)
   2. Teemu Kinnunen, Joni-Kristian Kämäräinen, Lasse Lensu, Heikki Kälviäinen, International Conference on Pattern Recognition, Unsupervised visual object categorisation via self-organisation, (2010)
   3. Teemu Kinnunen, Joni-Kristian Kämäräinen, Lasse Lensu, Jukka Lankinen, Heikki Kälviäinen, International Conference on Pattern Recognition, Making Visual Object Categorization More Challenging: Randomized Caltech-101 Data Set, (2010)
2. Scientific monographs
   1. Teemu Kinnunen, Master´s thesis, Unsupervised visual object categorization, (2008) pp. 73, pdf (2.75 MB)
   2. Teemu Kinnunen, Ph.D thesis, Bag-of-Features Approach to Unsupervised Visual Object Categorisation, (2011) pp. 106,

Do not hesitate to contact authors (e-mail, etc.) in order to retrieve copies or reprints of the following publications.