Scene identification method based on mixed depth structure

A scene recognition and depth technology, applied in the fields of image processing and computer vision, can solve the problems of high robustness and high computational efficiency

Active Publication Date: 2016-12-07
美辛软件科技南京有限公司
View PDF6 Cites 34 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In the task of scene recognition, there is still no recognition method with high computational efficiency, high ...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Scene identification method based on mixed depth structure
  • Scene identification method based on mixed depth structure
  • Scene identification method based on mixed depth structure

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] The present invention will be specifically introduced below in conjunction with specific embodiments.

[0040] A scene recognition method based on a hybrid depth structure, comprising the following steps:

[0041] Step 1: Randomly extract 400 image blocks from each scene picture, and standardize the image blocks in two ways: the first method is to subtract the average pixel value of the corresponding picture from the image blocks, and then normalize these image blocks as a whole, As shown in formula (11), where I n is the pixel value of the nth image block before normalization, I n1 ' is the pixel value normalized according to method 1, is the pixel mean value of the original image, I max ,I min They are the maximum value and minimum value of the pixels in the image respectively. Based on the local features extracted after this normalization method, the image encoding focuses on reflecting the color statistical characteristics of the image; the second method is to ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a scene identification method based on a mixed depth structure. A prior mixed depth identification frame is improved and applied to the task for scene identification, a depth self-encoder is adopted to automatically extract the local image block characteristic to replace the local characteristic extraction layer of a conventional mixed depth network to obtain an image block high-grade local characteristic; at the same time, spatial information is introduced to improve local characteristic coding layer of scene identification, and at the end, the scene is identified via depth discrimination network to improve the mixed depth mixed scene identification frame, so that the improved mixed depth scene is approximate to a convolutional neural network in the aspects of form and identification accuracy and is higher than a depth convolutional neural network in the aspect of calculation efficiency. In addition, the scene data is selectively expanded for the within-class difference and intra-class similarity of scene data, the construction robustness is high, and the method is suitable for the depth mixed scene identification model of a small data set.

Description

technical field [0001] It involves the fields of image processing and computer vision, especially a scene recognition method based on hybrid deep structure. Background technique [0002] Scene recognition is an important research direction in the field of computer vision. Scene recognition technology, that is, the computer automatically distinguishes the scene category of the collected images, which helps to deepen the computer's understanding of the scene and assist the computer to make other decisions. This technology is widely used in robot control, remote sensing image processing, intelligent monitoring and other fields. Aiming at the technical difficulties of scene recognition, domestic and foreign researchers have proposed many advanced algorithms. [0003] Recently, due to the development of computer technology, deep learning technology has achieved great success in the field of computer vision. The supervised deep learning network is composed of a multi-layer nonli...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06N3/02
CPCG06N3/02G06V20/35
Inventor 胡昭华姜啸远钱坤王珏
Owner 美辛软件科技南京有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap