Unsupervised feature learning using adaptive thresholding for object clustering


Unsupervised feature learning using adaptive thresholding for object clustering – This paper addresses the problem of learning the shape space of a high-dimensional (H) dimensional data set. To that end our contribution is a Bayesian framework that learns the shape space of the data set by solving a general Bayesian optimization problem. The framework shows that the H-dimensional data sets are not very compact, and hence a novel optimization problem is approached. The proposed framework is evaluated using the PASCAL VOC dataset, where it outperforms state-of-the-art methods in terms of accuracy and complexity.

We demonstrate how to recover an image with low-level features by solving low-level semantic image restoration problems. We propose a new approach that uses visual attention, by exploiting the fact that the human gaze can only appear in the visual domain. The technique is simple: we first extract low-level features from the image. Then the human gaze is recovered by means of our method. The proposed approach is evaluated on different synthetic datasets that provide very promising results.

Many recent advances in data collection, analytics and machine learning techniques rely on machine learning methods, which can be used to construct rich models for data. Many machine learning approaches try to incorporate a high-level representation into the data using a graphical model, but it is often hard to identify the key underlying model to the data. In this work, we propose using a deep convolutional network to classify the data and build a model. The model can then be used in classification tasks to learn the models’ properties. We use the model as a framework for analyzing the knowledge gained from the classification process, and we apply it to image classification tasks that involve classification of objects and their attributes in order to predict the attributes of objects that might be of interest. We report results of over 250 tasks on Image Recognition tasks that have the goal to classify objects and attributes from both human- and machine-generated images.

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Unsupervised feature learning using adaptive thresholding for object clustering

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  • Semantic Machine Meet Benchmark

    Learning to Race by Sipping a Dr PepperMany recent advances in data collection, analytics and machine learning techniques rely on machine learning methods, which can be used to construct rich models for data. Many machine learning approaches try to incorporate a high-level representation into the data using a graphical model, but it is often hard to identify the key underlying model to the data. In this work, we propose using a deep convolutional network to classify the data and build a model. The model can then be used in classification tasks to learn the models’ properties. We use the model as a framework for analyzing the knowledge gained from the classification process, and we apply it to image classification tasks that involve classification of objects and their attributes in order to predict the attributes of objects that might be of interest. We report results of over 250 tasks on Image Recognition tasks that have the goal to classify objects and attributes from both human- and machine-generated images.


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