Feature Extracting from Video Encoded for Searching Using Improved Methods

Abstract

There is a need for the transportation of visual content from a particular node to a computation unit, on a network limited by bandwidth for distributed visual analysis applications, like mobile visual search or Visual Sensor Networks (VSNs). The proposed system uses the video compression technique to transfer information over a communication network. The system is used for key point detection, descriptor calculation and feature matching. The proposed system provides key points to the server as side data to retrieve only the relevant points from the encoded video. In the proposed encoding approach, the User chooses the video from the dataset, extracts frames from the video and then extracts the key point of the selected frame and encodes the string or the key point on the extracted frame to then reconstruct or compress the video and upload it on the server. For decoding, whenever the user enters the search query on the browser, the server receives the search query. The server then selects the video, which is stored on the server, and extracts frames from the video, then key point or encoded data from the selected frame using decoding, and then compares the search query and the decoded data. If it is true, the video is sent to the user. The results show that the proposed approach offers significantly improved feature matching and image retrieval performance at given bitrates.
Copyright © 2017 Praise Worthy Prize - All rights reserved.