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Video retrieval method based on deep learning and Hash coding

A deep learning and hash coding technology, applied in the field of computer vision, can solve problems such as differences in picture features, and achieve the effect of improving speed, improving retrieval efficiency, and improving retrieval accuracy

Active Publication Date: 2019-05-21
北京远鉴信息技术有限公司
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AI Technical Summary

Problems solved by technology

Conventional video retrieval algorithms use different feature extraction algorithms when segmenting shots and extracting image frame features, which may lead to large differences in picture features in different frames of the same shot

Method used

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  • Video retrieval method based on deep learning and Hash coding

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Embodiment Construction

[0025] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. Such as figure 1 As shown, the overall process is divided into two parts: feature storage and video retrieval. Feature warehousing is an offline process, the purpose is to extract the representative features of the video from the video in the video library and store them in the video feature library; video retrieval is an online process, extracting video features from a single input video, and traversing and matching in the video feature library, The output match is the search result.

[0026] The feature extraction process in video retrieval and video storage is the same, such as figure 2 As shown, the specific steps are as follows:

[0027] Step 1, get the video file from the video storehouse, input in the feature extraction program;

[0028] Step 2, decode video into video frame with video decoding storehouse (such as FFMPEG), when will reduce co...

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Abstract

The invention discloses a deep learning and Hash coding video retrieval method, and the method is characterized by comprising the following steps of: performing shot segmentation and feature extraction by using the same feature; in video retrieval, shot segmentation takes video frames of the same shot as redundant frames to be processed so as to reduce the calculated amount and improve the retrieval efficiency, and features obtained through a shot feature extraction algorithm are taken as the basis of shot segmentation so as to solve the problem that features of different frames in the same shot are different. The method has the advantages that the video retrieval at the lens level can be realized, and the retrieval accuracy can be improved; The video shot retrieval accuracy in the aspectof video retrieval capacity reaches 95% or above, pure binary operation is adopted, and the speed can be increased by four times.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a video retrieval method based on deep learning and hash coding. Background technique [0002] At present, video retrieval includes two types, namely text-based video retrieval (Text Based Video Retrieval, RBVR) and content-based video retrieval (Content Based Video Retrieval, CBVR). Text-based video retrieval requires users to input keywords, and then the system returns images related to the input keywords, and sorts them according to the degree of relevance to the keywords. This retrieval method is widely used in current Internet applications, but in many cases, users cannot accurately describe the video content they want to retrieve in words. [0003] The patent application with the publication number CN109033121A discloses a video retrieval method based on cloud storage, which includes the following steps: intercepting the video segment information to be ...

Claims

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Application Information

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IPC IPC(8): G06F16/783G06F16/732G06K9/62
Inventor 孔彦伭剑辉赵玉军王黎明
Owner 北京远鉴信息技术有限公司
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