DISK: learning local features with policy gradient
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The ever increasing number of digital images in both public and private collections urges on the need for generic image content analysis systems. These systems need to be capable to capture the content of images from both scenes and objects, in a compact w ...
Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...
State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our ...
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis appro ...
In this paper we study the behavior of local descriptor object recognition methods with respect to 3D geometric transformations and image resolution variations. As expected performance decreases with accentuated perspective and decrease in resolution. To i ...
In this paper we study the behavior of local descriptor object recognition methods with respect to 3D geometric transformations and image resolution variations. As expected performance decreases with accentuated perspective and decrease in resolution. To i ...
This paper presents a novel approach for visual scene modeling and classification, investigating the combined use of text modeling methods and local invariant features. Our work attempts to elucidate (1) whether a text-like \emph{bag-of-visterms} represent ...
