Abstract. We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within

This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM

Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity

We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical modelling. Unsupervised learning can be motivated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA

the perspective of factor analysis, a paradigmatic technique of unsupervised learning. We use factor analysis to re-interpret a standard view of adaptation, and apply our new model to some recent data on adaptation in the domain of face discrimination. 1

This paper presents a method for unsupervised learning of morphology that exploits the syntactic categories of words. Previous research [4][12] on learning of morphology and syntax has shown that both kinds of knowledge affect each other making it possible to use one type of knowledge to help

This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a

ion Pyramid is an architecture for iterative image interpretation that has been inspired by the information processing principles of the visual cortex. We present an unsupervised learning algorithm for the design of its feed-forward connectivity that is based on Hebbian weight updates

Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences is very useful for a variety of tasks, such as video surveillance, objectlevel video summarization, video indexing, digital library organization, etc. However, it remains a challenging task for computers to achieve robust action recognition due to cluttered background, camera motion, occlusion, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and

This paper discusses the unsupervised learning problem. An important part of the unsupervised learning problem is determining the number of constituent groups (components or classes) which best describes some data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning