Cortical-based hierarchical clustering algorithm for image classification


Cortical-based hierarchical clustering algorithm for image classification – A key problem in many computer vision applications is the detection or segmentation of unknown objects from image. In order to tackle this challenge, we propose a novel and efficient clustering algorithm for object identification and extraction. The key idea is to first learn a graph-theoretic model of different clusters, and then train a Convolutional Neural Net (CNN) to recognize unknown objects (cannot distinguish any object). In this work, we propose a novel supervised learning approach that trains CNN-based models, and then optimizes their performance by minimizing the dimension of the network and the number of objects. We demonstrate that this approach outperforms both traditional clustering methods and our best-known segmentation method, i.e. kLSTM-SVM, in several real-world datasets, and is faster than the best known method.

In this work, we investigate the problem of learning an optimal policy if the optimal policy is given by a good policy, or a poor policy. Our main ideas are: 1) We use a regularizer to model the nonconvex norm, and 2) We use a probabilistic optimization to optimize a Gaussian density function to estimate the optimal nonconvex policy. We show that our policy approximation algorithms outperform many state-of-the-art policy estimates in terms of performance and scalability, and that we can obtain a high-dimensional policy that performs well in practice. Our method is more robust to outliers that are present in the data, and can be extended to handle large graphs. We experimentally show that our method is very efficient in several settings (optimal policy, low-hanging fruit, and nonconvex policy), and show that it performs well under both settings, even in real-data scenarios.

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Cortical-based hierarchical clustering algorithm for image classification

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  • Comparing Deep Neural Networks to Matching Networks for Age Estimation

    A Randomized Nonparametric Bayes Method for Optimal Bayesian RankingIn this work, we investigate the problem of learning an optimal policy if the optimal policy is given by a good policy, or a poor policy. Our main ideas are: 1) We use a regularizer to model the nonconvex norm, and 2) We use a probabilistic optimization to optimize a Gaussian density function to estimate the optimal nonconvex policy. We show that our policy approximation algorithms outperform many state-of-the-art policy estimates in terms of performance and scalability, and that we can obtain a high-dimensional policy that performs well in practice. Our method is more robust to outliers that are present in the data, and can be extended to handle large graphs. We experimentally show that our method is very efficient in several settings (optimal policy, low-hanging fruit, and nonconvex policy), and show that it performs well under both settings, even in real-data scenarios.


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