search query: @keyword pattern recognition / total: 31
results-list
reference: 6 / 31
« previous | next »
Author:Hao, Tele
Title:Gated Boltzmann machine in texture modeling
Publication type:Master's thesis
Publication year:2012
Pages:(11) + 70 s. + liitt. 3      Language:   eng
Department/School:Tietotekniikan laitos
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Karhunen, Juha
Instructor:Raiko, Tapani
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions
close

Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

Logging on to the customer computers

  • Aalto University staff members log on to the customer computer using the Aalto username and password.
  • Other customers log on using a shared username and password.

Opening a thesis

  • On the desktop of the customer computers, you will find an icon titled:

    Aalto Thesis Database

  • Click on the icon to search for and open the thesis you are looking for from Aaltodoc database. You can find the thesis file by clicking the link on the OEV or OEVS field.

Reading the thesis

  • You can either print the thesis or read it on the customer computer screen.
  • You cannot save the thesis file on a flash drive or email it.
  • You cannot copy text or images from the file.
  • You cannot edit the file.

Printing the thesis

  • You can print the thesis for your personal study or research use.
  • Aalto University students and staff members may print black-and-white prints on the PrintingPoint devices when using the computer with personal Aalto username and password. Color printing is possible using the printer u90203-psc3, which is located near the customer service. Color printing is subject to a charge to Aalto University students and staff members.
  • Other customers can use the printer u90203-psc3. All printing is subject to a charge to non-University members.
Location:P1 Ark Aalto     | Archive
Keywords:machine learning
unsupervised learning
deep learning
Boltzmann machine
texture analysis
pattern recognition
Abstract (eng): Recently, a new type of neural network method, namely deep learning, was discovered, which yielded excellent results in various tasks such as handwritten digit recognition, speech recognition and facial expression recognition.
Different from traditional multi-layer perceptron learning methods (MLP), an unsupervised pre-training step before supervised learning is of huge importance in learning successful features.
Also, it is argued that the deep architecture has more expressive power comparing to the conventional shallow networks, such as support vector machine or multi-layer perceptron's.

Even though deep learning has yielded a large amount of world-class records in different tasks, there is little research on how the deep network can be used in texture analysis.

For this particular problem, we consider the problem of modelling complex texture information using undirected probabilistic graphical models.
Texture is a special type of data that one can better understand by considering its local structure.
For that purpose, we propose a convolutional variant of the Gaussian gated Boltzmann machine (GGBM), inspired by the co-occurrence matrix in traditional texture analysis.
We also link the proposed model to a much simpler Gaussian restricted Boltzmann machine where convolutional features are computed as a pre-processing step.
The usefulness of the model is illustrated in texture classification and reconstruction experiments.
ED:2013-01-07
INSSI record number: 45744
+ add basket
« previous | next »
INSSI