Topic Discovery Models

Algorithm Overview

• Input: Large collection of unlabeled images

• Generate multiple segmentations – Normalized Cuts varying K and image scale

• Obtain visual words for each segment – Quantized SIFT descriptors

– Bag-of-words

• Topic discovery models

Topic Models

• Analyze collection of segments and discover ‘topics’

• Topics: visually similar, frequently occuring

Topic Models

• Why topic models?

• Insight:

– segments corresponding to objects will be represented by coherent groups (topics)

– segments overlapping object boundaries will need to be explained by a mixture of several groups (topics)

Topic Models

• Originally designed for text analysis

– Discovering topics in documents

• Probabilistic Latent Semantic Analysis (pLSA)

• Latent Dirichlet Allocation (LDA)

• Following slides courtesy of David Blei

Topic Models

Topic Models

Topic Models

• 100-topic LDA over 17,000 Science articles

Topic Models

• 100-topic LDA over 17,000 Science articles

Topic Models

• Discovering objects in images

LDA

LDA

LDA

LDA

LDA

LDA

LDA

LDA

LDA

LDA

• Gibbs sampling

– obtain samples from probability distribution

– for each variable, condition on all other variables and compute posterior

– collect sequence of samples and estimate probabilities

• Collapsed Gibbs sampling

– analytically compute the posterior of certain variables

LDA

• Collapsed Gibbs sampling

– integrate out theta/beta parameters

– sample topic assignments z for each word using Gibbs sampling

– estimate parameters from these samples

pLSA

• Simple topic model

– topic proportions not latent variables

– learns topic mixtures for training documents

– problems with overfitting due to large parameter space