Supervised learning of large perceptual organization: graph spectral partitioning and learning automata
نویسندگان
چکیده
منابع مشابه
Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata
ÐPerceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, ...
متن کاملGraph Partitioning Using Learning Automata
Given a graph G, we intend to partition its nodes into two sets of equal size so as to minimize the sum of the cost of the edges having end-points in different sets. This problem, called the uniform Graph Partitioning Problem (GPP), is known to be NP-Complete. In this paper we propose the first reported learning-automaton based solution to the problem. We compare this new solution to various re...
متن کاملSemi-supervised Learning with Spectral Graph Wavelets
We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spectral graph wavelets behave like discrete multiscale differential operators on graphs, and thus can sparsely approximate piecewise smooth sign...
متن کاملTransductive Learning via Spectral Graph Partitioning
We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning methods, the training problem has a meaningful relaxation that can be solved globally optimally using spectral methods. We propose an algorithm that robustly achieves good generalization performance and that can be train...
متن کاملSupervised Learning of Graph Structure
Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2000
ISSN: 0162-8828
DOI: 10.1109/34.857006