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5 Classification of painting styles Van Gogh Shishkin Cluster I M - current mixture model Current models in M Aivazovsky Old models removed from M Models obtained from tentative split Picasso Rembrandt M 0

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Presentation Transcript
slide1

5

Classification of painting styles

Van Gogh

Shishkin

Cluster I

M - current mixture model

Current models in M

Aivazovsky

Old models removed from M

Models obtained from tentative split

Picasso

Rembrandt

M0

35 pictures (about 600x800 pixels) drawn by 5 different painters. The algorithm successfully identifies the painters by their drawing style.

Score(M) = P(M) JS(M1,M2)

Cluster II

M

“Tentative” split

JS

M2

M1

1

Image window

Wavelet transform

Normalized coefficient histograms

2

5

4

3

Unsupervised Clustering of Images using their Joint Segmentation

Yevgeny Seldin, Sonia Starik, Michael Werman

School of Computer Science and Engineering

The Hebrew University of Jerusalem, Israel

www.cs.huji.ac.il/~seldin/SCTV2003

{seldin,starik,werman}@cs.huji.ac.il

1

Abstract

We present a method for unsupervised content based classification of images. Our major idea is to start from joint segmentation of all the images in the input set. We then draw an analogy between segments/words and images/documents and apply advanced algorithms from the field of unsupervised document classification to cluster the images.

Outline

  • Input: set of images
    • photos from traveling, image database organization, movie frames
  • Preprocessing step:Texture modeling
    • cover each image by an overlapping net of small square windows
    • model each window as a texture
  • Step1: Unsupervised joint segmentation
    • represent the images as a mixture of a small number of textures.
  • Step 2: Image clustering based on the segmentation map: Use co-occurrence statistics of mixture components and images in order to cluster the images

3

Step 1: Unsupervised joint image segmentation

(Unsupervised feature selection)

  • Represent all the images in the set with single mixture model M.
  • Use forced hierarchy deterministic annealing framework for soft hierarchical top-down segmentation at increasing levels of resolution

Schematic illustration of segmentation framework

Example

Classification

based on the

Segmentation

map

Joint

Segmentation

(Texture based)

6

Classification of the Hebrew university campus views

Segmented images

(Similar textures have the same color)

Classified images

Original images

4

2

Preprocessing: Texture modeling

  • Texture parametric model: Set of histograms of wavelet coefficients, normalized to probability distributions
  • Distance between textures
    • Kullback-Leibler divergence
  • Centroid Model
    • A centroid model M for a weighted set of textures is a normalized weighted sum of wavelet histograms Hi

Other statistical properties, for example color histograms for a window, can be used instead or together with texture modeling

Step 2: Image classification according to the segmentation map

(Unsupervised classification based on the selected features)

Indoor scenes

Perspective views

Idea: Use image-document, model-word analogy

Goal: represent the images with a small number of clusters s.t. the distribution of models inside the clusters will be maximally close to the original distribution of the models inside the images

Solution: sequential Information Bottleneck algorithm of Slonim, Friedman, Tishby

2. Reassign it to cluster C* for which I(C;M) is maximized

1. Pick a random image I from a random cluster C

Tree branches on sky background

Bushes and flowers, close view

In this example the algorithm classifies 55 view images (640x480 pixels) by their content