2,675 research outputs found
A binary self-organizing map and its FPGA implementation
A binary Self Organizing Map (SOM) has been designed and
implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of the binary features
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously, leading to more accurate and coherent predictions.
Structural support vector machines (SSVMs) are nonprobabilistic models that
optimize a joint input-output function through margin-based learning. Because
SSVMs generally disregard the interplay between unary and interaction factors
during the training phase, final parameters are suboptimal. Moreover, its
factors are often restricted to linear combinations of input features, limiting
its generalization power. To improve prediction accuracy, this paper proposes:
(i) Joint inference and learning by integration of back-propagation and
loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM
factors to neural networks that form highly nonlinear functions of input
features. Image segmentation benchmark results demonstrate improvements over
conventional SSVM training methods in terms of accuracy, highlighting the
feasibility of end-to-end SSVM training with neural factors
A Binary Neural Shape Matcher using Johnson Counters and Chain Codes
In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes
Binary object recognition system on FPGA with bSOM
Tri-state Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, has been used for classification rather than clustering in this paper. The major contribution here is the demonstration of the potential use of the modified bSOM in security surveillance, as a recognition system on FPGA
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