74,754 research outputs found
Experience-driven formation of parts-based representations in a model of layered visual memory
Growing neuropsychological and neurophysiological evidence suggests that the
visual cortex uses parts-based representations to encode, store and retrieve
relevant objects. In such a scheme, objects are represented as a set of
spatially distributed local features, or parts, arranged in stereotypical
fashion. To encode the local appearance and to represent the relations between
the constituent parts, there has to be an appropriate memory structure formed
by previous experience with visual objects. Here, we propose a model how a
hierarchical memory structure supporting efficient storage and rapid recall of
parts-based representations can be established by an experience-driven process
of self-organization. The process is based on the collaboration of slow
bidirectional synaptic plasticity and homeostatic unit activity regulation,
both running at the top of fast activity dynamics with winner-take-all
character modulated by an oscillatory rhythm. These neural mechanisms lay down
the basis for cooperation and competition between the distributed units and
their synaptic connections. Choosing human face recognition as a test task, we
show that, under the condition of open-ended, unsupervised incremental
learning, the system is able to form memory traces for individual faces in a
parts-based fashion. On a lower memory layer the synaptic structure is
developed to represent local facial features and their interrelations, while
the identities of different persons are captured explicitly on a higher layer.
An additional property of the resulting representations is the sparseness of
both the activity during the recall and the synaptic patterns comprising the
memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in
Computational Neuroscience (Special Issue on Complex Systems Science and
Brain Dynamics),
http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009
Local-HDP:Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios
We introduce a non-parametric hierarchical Bayesian approach for open-ended
3D object categorization, named the Local Hierarchical Dirichlet Process
(Local-HDP). This method allows an agent to learn independent topics for each
category incrementally and to adapt to the environment in time. Hierarchical
Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform
low-level features to high-level conceptual topics for 3D object
categorization. However, the efficiency and accuracy of LDA-based approaches
depend on the number of topics that is chosen manually. Moreover, fixing the
number of topics for all categories can lead to overfitting or underfitting of
the model. In contrast, the proposed Local-HDP can autonomously determine the
number of topics for each category. Furthermore, the online variational
inference method has been adapted for fast posterior approximation in the
Local-HDP model. Experiments show that the proposed Local-HDP method
outperforms other state-of-the-art approaches in terms of accuracy,
scalability, and memory efficiency by a large margin. Moreover, two robotic
experiments have been conducted to show the applicability of the proposed
approach in real-time applications.Comment: 13 page
Local-HDP:Interactive Open-Ended 3D Object Categorization
We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin
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