Download presentation

Presentation is loading. Please wait.

Published byReece Jone Modified over 3 years ago

1
Organizing a spectral image database by using Self-Organizing Maps Research Seminar 7.10.2005 Oili Kohonen

2
Motivation? Image retrieval from conventional databases since 1990's... many efficient techniques have been developed However, efficient techniques for querying images from spectral image database does not exist. Due to the high amount of data in the case of spectral images, the efficient techniques will be needed.

3
Spectral imaging? Metameric imaging: cheap and practical way to achieve a color match. Spectral imaging: needed to achieve a color match for all observers across the changes in the illumination.

4
Principle of SOM: The Self-Organizing Map (SOM) algorithm: Is an unsupervised learning algorithm. Defines mapping from high-dimensional data into lower-dimensional data. SOM: Consists of arranged units (or neurons), which are represented by weight vectors. Units are connected to each other by neighborhood relation.

5
Principle of SOM: SOM Algorithm: begin Initialize the SOM for i = 1 : number of epochs take input vector x randomly from the training data; find the BMU for x; update the weight vectors of the map; decrease the learning rate & neighborhood function; end;

6
Principle of SOM: finding the BMU Mathematically the BMU is defined for input data vector, x, as follows: Euclidean distance is a typically used distance measure.

7
Principle of SOM: updating the weight vectors Learning rate: product of learning rate parameter & neighborhood function:

8
Principle of SOM: neighborhood function Neighborhood function h(t) has to fullfill the following two requirements: It has to be symmetric about the maximum point (BMU). It's amplitude has to decrease monotonically with an increasing distance from BMU. Gaussian function is a typical choice for h(t)

9
Principle of SOM: Lattice structure Lattice structures: hexagonal & rectangular

10
Searching Technique: Constructing histogram database Train SOM Find BMU for each pixel in an image Generate BMU-histogram & normalize it by the number of pixels in an image Repeat steps 2 & 3 for all images in a spectral image database Save histogram database with the information of SOM-map

11
Searching Technique: making a search Choose an image and generate its histogram. Calculate the distances between the generated histogram and the existing histogram database. Order images by these distances. The results of the search are shown to user as RGB-images

12
Searching techniques: One-dimensional SOM:

13
Searching techniques: Two-dimensional histogram-trained SOM

14
Distance Calculations: H1 & H2 are the compared histograms L1 & L2 are the indices of max. values| H3=(H1+H2)/2

15
Experiments: One-dimensional SOM for unweighted images One-dimensional SOM for images weighted by HVS-function Two-dimensional SOM From histogram data From spectral data Human Visual Sensitivity-function (Unweighted images) (Unweighted and weighted images)

16
The Used Database: 106 images: 61 components, spectral range from 400 nm to 700 nm at 5 nm interval.

17
Training of the SOMs: 10 000 spectra were selected randomly from each image. 2 000 000 & 4 000 000 epochs in ordering & fine tuning phases, respectively. Unit sizes: 50 – chosen empirically 49 – to have comparable results with 1D-SOM 14*14 map in the case of histogram-trained SOM

18
Results: 1d-SOM, Unweighted images Pure data Multiplied data The distance measure: Euclidean distance

19
Results: 1D, Unweighted images Energy K-L Peak DPD JD

20
Results: 1D, Weighted images Energy K-L Peak DPD JD

21
Conclusions I: The “structure” of the database is different for weighted and unweighted images. The “best” results were got by using euclidean distance and Jeffrey divergence. Importance of normalization?? * Better results with Euclidean distance & DPD * Worse results with Jeffrey divergence

22
Results: 2D, Unweighted spectral data Euclidean Energy K-L Peak DPD JD

23
Results: 2D, Weighted spectral data Euclidean Energy K-L Peak DPD JD

24
Conclusions II: In the case of two-dimensional SOM better results are achieved by using non-weighted images. When the weighted images are used, the use of 1D- SOM seems to be more reasonable.

25
Results: histogram-trained 2D-SOM Euclidean Energy K-L Peak DPD JD

26
Connections between images and histograms: non-weighted weighted

27
Past, Present & Future: Past: What you have seen so far... Present: Texture features in addition to color features Future: Testing the effect of different metrics in ordering and fine-tuning phases (during the training of SOM)

28
Questions: ? Thank you for not asking any... =)

Similar presentations

OK

November 9, 2010Neural Networks Lecture 16: Counterpropagation 1 Unsupervised Learning So far, we have only looked at supervised learning, in which an.

November 9, 2010Neural Networks Lecture 16: Counterpropagation 1 Unsupervised Learning So far, we have only looked at supervised learning, in which an.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Ppt on inhabiting other planets that can support Ppt on project rhino in indian Mis ppt on hospital waste Download ppt on trigonometric functions for class 11 Ppt on environmental issues in chennai Ppt on national festivals of india Ppt on circles for class 9 free download Ppt on hydrogen fuel cell vehicles price Ppt on power generation using footsteps sound Ppt on central administrative tribunal