A method of extracting picture features by using a binary bottleneck neural network

A neural network and image feature technology applied in the field of video processing to achieve good performance

Active Publication Date: 2017-12-12
央视国际网络无锡有限公司
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Problems solved by technology

[0006] The purpose of the present invention is to provide a method for extracting image features using a bin

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  • A method of extracting picture features by using a binary bottleneck neural network
  • A method of extracting picture features by using a binary bottleneck neural network
  • A method of extracting picture features by using a binary bottleneck neural network

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Embodiment Construction

[0028] like figure 1 A method for extracting image features using a binary bottleneck neural network, comprising the steps of:

[0029] Step 1: set up a binary bottleneck neural network, this binary bottleneck neural network comprises input layer, hidden layer, output layer and image layer; Described hidden layer comprises the first hidden layer, the second hidden layer and the third hidden layer;

[0030] Step 2: After the picture is acquired by the camera, the picture is uniformly processed to make the picture a resolution size suitable for processing in the binary bottleneck neural network, and the unified processing includes zoom-in processing and zoom-out processing;

[0031] When the pictures in the 8-bit encoding format are uniformly processed, since the pixel values ​​of the pictures in the 8-bit encoding format range from 0 to 255, all the pixel values ​​in the pictures in the 8-bit encoding format are divided by 255 during processing. , so that it is normalized to t...

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Abstract

The invention provides a method of extracting picture features by using a binary bottleneck neural network and belongs to the technical field of video processing. By building a binary bottleneck neural network, a picture is automatically extracted as a feature vector including multiple binary bits. When comparing two images for obtaining the similarity degree thereof, only the binary feature vectors of the two pictures need to be compared and then the Hamming distance between the two binary feature vectors is calculated; the smaller the Hamming distance, the higher the similarity degree of the two pictures. The method solves the technical problem in extracting binary feature vectors of images. The method calculates feature binary sequences of images and good performance can be obtained without manual design based on experience of researchers; image feature binary sequences calculated by the method can be used for calculating the similarity of images rapidly, so that the method is of great importance for similarity retrieval of pictures and video.

Description

technical field [0001] The invention belongs to the technical field of video processing, in particular to a method for extracting picture features by using a binary bottleneck neural network. Background technique [0002] Image data is a typical unstructured data, and there are difficulties in image database query, retrieval, and similarity comparison. This is caused by several reasons: 1) The dimension of image data is high, and the resolution of general high-definition images It can reach about 2 million pixels, and the resolution of ultra-clear images can reach as much as 8 million pixels; 2) The semantics contained in the image are difficult to obtain directly from the data. For example, an image contains a car, and humans can easily This image semantics is observed, but it is difficult for computers to obtain this semantics. Only through complex algorithms such as artificial intelligence can the specific semantics of the car in the image be recognized. [0003] In orde...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06N3/04
CPCG06V10/462G06N3/045G06F18/213G06F18/22
Inventor 张勇朱立松
Owner 央视国际网络无锡有限公司
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