MPEG-2 Video Archive System for Live-Broadcasting Using IP over ATM and a Study on Video Retrieval System for Large-Scale Archives

1999 ◽  
Author(s):  
Kazuhiko Matsubayashi ◽  
Haruo Sakurada ◽  
Kengo Nomura ◽  
Teppei Shimoyama ◽  
Takashi Kato ◽  
...  
Keyword(s):  
Large Scale ◽  
Retrieval System ◽  
Video Retrieval ◽  
Archive System ◽  
Ip Over Atm

Correlation-Based Ranking for Large-Scale Video Concept Retrieval

2010 ◽  
Vol 1 (4) ◽  
pp. 60-74 ◽  
Author(s):  
Lin Lin ◽  
Mei-Ling Shyu
Keyword(s):  
Large Scale ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multiple Correspondence Analysis ◽  
Multimedia Data ◽  
Ranking Algorithm ◽  
Video Content ◽  
Semantic Concepts ◽  
Efficient Retrieval ◽  
High Level

Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transaction weights converted from correlations. Finally, a user interface is implemented in a video retrieval system that allows the user to enter his/her interested concept, searches videos based on the target concept, ranks the retrieved video segments using the proposed ranking algorithm, and then displays the top-ranked video segments to the user. Experimental results on 30 concepts from the TRECVID high-level feature extraction task have demonstrated that the presented video retrieval system assisted by the proposed ranking algorithm is able to retrieve more video segments belonging to the target concepts and to display more relevant results to the users.

Correlation-Based Ranking for Large-Scale Video Concept Retrieval

2012 ◽  
pp. 28-42
Author(s):  
Lin Lin ◽  
Mei-Ling Shyu
Keyword(s):  
Large Scale ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multiple Correspondence Analysis ◽  
Multimedia Data ◽  
Ranking Algorithm ◽  
Video Content ◽  
Semantic Concepts ◽  
Efficient Retrieval ◽  
High Level

Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transaction weights converted from correlations. Finally, a user interface is implemented in a video retrieval system that allows the user to enter his/her interested concept, searches videos based on the target concept, ranks the retrieved video segments using the proposed ranking algorithm, and then displays the top-ranked video segments to the user. Experimental results on 30 concepts from the TRECVID high-level feature extraction task have demonstrated that the presented video retrieval system assisted by the proposed ranking algorithm is able to retrieve more video segments belonging to the target concepts and to display more relevant results to the users.

Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval

2017 ◽  
Vol 31 (09) ◽  
pp. 1750029 ◽  
Author(s):  
Kaiyang Liao ◽  
Fan Zhao ◽  
Yuanlin Zheng ◽  
Congjun Cao ◽  
Mingzhu Zhang
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Retrieval System ◽  
Video Retrieval ◽  
Large Datasets ◽  
Large Scale Data ◽  
Image Retrieval System ◽  
Labeling Method ◽  
Parallel Clustering ◽  
Scale Data

Using clustering method to detect useful patterns in large datasets has attracted considerable interest recently. The HKM clustering algorithm (Hierarchical K-means) is very efficient in large-scale data analysis. It has been widely used to build visual vocabulary for large scale video/image retrieval system. However, the speed and even the accuracy of hierarchical K-means clustering algorithm still have room to be improved. In this paper, we propose a Parallel N-path quantification hierarchical K-means clustering algorithm which improves on the hierarchical K-means clustering algorithm in the following ways. Firstly, we replace the Euclidean kernel with the Hellinger kernel to improve the accuracy. Secondly, the Greedy N-best Paths Labeling method is adopted to improve the clustering accuracy. Thirdly, the multi-core processors-based parallel clustering algorithm is proposed. Our results confirm that the proposed clustering algorithm is much faster and more effective.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

scholarly journals Large-Scale Video Retrieval via Deep Local Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Query Image ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Storage Cost ◽  
Extraction Algorithm ◽  
Feature Aggregation ◽  
And Storage

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

scholarly journals HandVR: a hand-gesture-based interface to a video retrieval system

2014 ◽  
Vol 9 (7) ◽  
pp. 1717-1726 ◽  
Author(s):  
Serkan Genç ◽  
Muhammet Baştan ◽  
Uğur Güdükbay ◽  
Volkan Atalay ◽  
Özgür Ulusoy
Keyword(s):  
Retrieval System ◽  
Video Retrieval ◽  
Hand Gesture

scholarly journals A Distributed Content-Based Video Retrieval System for Large Data-sets

2021 ◽  
Author(s):  
ElMehdi SAOUDI ◽  
Said Jai Andaloussi
Keyword(s):  
Real Time ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Data Set ◽  
Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

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