4,532 research outputs found
A Self-Organizing Neural System for Learning to Recognize Textured Scenes
A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar
A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877
A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar
A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
ARTEX: A Self-Organizing Architecture for Classifying Image Regions
A self-organizing architect is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to diflicult real-world image classification problems, including classification of synthetic aperture radar and natural textural images, and outperforms a recent state-of-the-art system at classifying natural textures.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100, N00014-95-1-0409); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-4015, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877). an
An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
Most semi-supervised learning (SSL) models entail complex structures and
iterative training processes as well as face difficulties in interpreting their
predictions to users. To address these issues, this paper proposes a new
interpretable SSL model using the supervised and unsupervised Adaptive
Resonance Theory (ART) family of networks, which is denoted as SSL-ART.
Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of
prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy
ARTMAP structure to map the established prototype nodes to the target classes
using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is
devised to associate a prototype node with more than one class label. The main
advantages of SSL-ART include the capability of: (i) performing online
learning, (ii) reducing the number of redundant prototype nodes through the OtM
mapping scheme and minimizing the effects of noisy samples, and (iii) providing
an explanation facility for users to interpret the predicted outcomes. In
addition, a weighted voting strategy is introduced to form an ensemble SSL-ART
model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART,
assigns {\color{black}a different weight} to each class based on its
performance pertaining to the corresponding class. The aim is to mitigate the
effects of training data sequences on all SSL-ART members and improve the
overall performance of WESSL-ART. The experimental results on eighteen
benchmark data sets, three artificially generated data sets, and a real-world
case study indicate the benefits of the proposed SSL-ART and WESSL-ART models
for tackling pattern classification problems.Comment: 13 pages, 8 figure
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