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Deep-reinforcement-learning-based graphic binary feature learning method and device

A technology of reinforcement learning and feature learning, applied in the field of computer vision and machine learning.

Active Publication Date: 2018-06-19
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the binary feature learning method in the related art does not consider the relationship between the bits of the feature, and each bit of the independent learning feature may lead to feature bits located at the boundary of the binarization, and these bits are easily affected by noise. Therefore, the robustness of the feature is not strong, and it needs to be solved

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  • Deep-reinforcement-learning-based graphic binary feature learning method and device
  • Deep-reinforcement-learning-based graphic binary feature learning method and device
  • Deep-reinforcement-learning-based graphic binary feature learning method and device

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Embodiment Construction

[0037] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0038] The following describes the method based on deep reinforcement learning according to the embodiment of the present invention with reference to the drawings Figure II value feature learning method and device, firstly, the deep reinforcement learning based Figure II value feature learning method.

[0039] figure 1 It is the deep reinforcement learning based on the embodiment of the present invention Figure II value feature learning method

[0040] Such as figure 1 As shown, the deep reinforcement learning-based Figur...

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Abstract

The invention discloses a deep-reinforcement-learning-based graphic binary feature learning method and device. The method comprises: an actual depth value feature of an image is extracted; according to an inter-bit relationship of deep reinforcement learning, mining is carried out to obtain base quantities, including a state, a transfer proof, an action, and a reward, of an inter-bit relationshipmining network, wherein the base quantities, thereby obtaining an inter-bit relationship mining network by training; and extraction is carried out to obtain a robust feature based on the inter-bit relationship mining network and a feature extraction network of mutual information. With the method disclosed by the invention, the robust feature is extracted by using the inter-bit relationship miningnetwork and the feature extraction network of mutual information, so that the feature robustness is improved effectively.

Description

technical field [0001] The present invention relates to the technical fields of computer vision and machine learning, in particular to a deep reinforcement learning-based Figure II Value feature learning method and device. Background technique [0002] One of the very important basic problems in the field of computer vision is the visual recognition task, which is widely used in various visual tasks, such as general object recognition, scene recognition, face recognition and fingerprint recognition. The visual recognition task is essentially a pattern recognition task, which has the characteristics of high data dimensionality, large amount of data, and large data differences. The basis and basis of visual recognition tasks are visual features, which refer to feature vectors corresponding to pictures. A "good" feature satisfies the characteristics that the similarity of the feature vectors of the same kind of pictures is strong, and the similarity of the feature vectors of ...

Claims

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Application Information

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IPC IPC(8): G06K9/38G06K9/46G06K9/62G06N3/04
CPCG06V10/28G06V10/462G06N3/045G06F18/24G06F18/214
Inventor 鲁继文周杰段岳圻
Owner TSINGHUA UNIV