Web Image Gathering with Probabilistic Models

Probabilistic Image Modeling with Dependency-tree Hidden Markov Models

In this paper, we investigate some properties of a new type of 2D Hidden Markov Model, based on the notion of Dependency Tree. DT-HMMs avoid the complexity of regular 2D HMMs by changing the double horizontal and vertical spatial dependencies into a random uni-directional dependency, either horizontal or vertical. We explore various issues about the effect of this random choice. This type of pr...

Image analysis based on probabilistic models

This paper discusses random field based image classification methods, and in particular conditional random fields (CRF), for topographic mapping. A short review of the CRF principles reveals their main advantages, namely the possibility to incorporate local context into the classification to quantify the quality of the results in terms of probabilities. Three examples, the classification of poi...

Bayesian Probabilistic Models for Image Retrieval

In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is based on the predictive densities of generative probabilistic models modelling the density of image features. We evaluate the proposed methodology and compare it against two state of the art image retrieval systems using a well known image collec...

Probabilistic Scene Models for Image Interpretation

Image interpretation describes the process of deriving a semantic scene description from an image, based on object observations and extensive prior knowledge about possible scene descriptions and their structure. In this paper, a method for modeling this prior knowledge using probabilistic scene models is presented. In conjunction with Bayesian Inference, the model enables an image interpretati...

Generative probabilistic models for image retrieval