Chrome Extension
WeChat Mini Program
Use on ChatGLM

A Robust SIFT-based Descriptor for Video Classification.

Proceedings of SPIE--the International Society for Optical Engineering(2015)

Sharif Univ Technol

Cited 1|Views9
Abstract
Voluminous amount of videos in today's world has made the subject of objective (or semi-objective) classification of videos to be very popular. Among the various descriptors used for video classification, SIFT and LIFT can lead to highly accurate classifiers. But, SIFT descriptor does not consider video motion and LIFT is time-consuming. In this paper, a robust descriptor for semi-supervised classification based on video content is proposed. It holds the benefits of LIFT and SIFT descriptors and overcomes their shortcomings to some extent. For extracting this descriptor, the SIFT descriptor is first used and the motion of the extracted keypoints are then employed to improve the accuracy of the subsequent classification stage. As SIFT descriptor is scale invariant, the proposed method is also robust toward zooming. Also, using the global motion of keypoints in videos helps to neglect the local motions caused during video capturing by the cameraman. In comparison to other works that consider the motion and mobility of videos, the proposed descriptor requires less computations. Obtained results on the TRECVIT 2006 dataset show that the proposed method achieves more accurate results in comparison with SIFT in content-based video classifications by about 15 percent.
More
Translated text
Key words
Robust Video Descriptor,SIFT,Video Classification,LIFT
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了一种基于SIFT的鲁棒视频分类描述符,融合了LIFT和SIFT描述符的优点,并克服了它们的不足,通过利用关键点的运动信息提高了分类准确性。

方法】:该方法首先使用SIFT描述符提取关键点,然后利用这些关键点的运动信息来增强后续分类阶段的准确性。

实验】:在TRECVIT 2006数据集上的实验结果显示,该方法比仅使用SIFT描述符在基于内容的视频分类中提高了大约15%的准确性。