PIRIA: a general tool for indexing, search, and retrieval of multimedia content

2004 ◽  
Author(s):  
Magali Joint ◽  
Pierre-Alain Moellic ◽  
P. Hede ◽  
P. Adam
Keyword(s):  
Multimedia Content ◽  
General Tool ◽  
Search And Retrieval

Requirements to a Search Engine for Semantic Multimedia Content

2014 ◽  
Vol 5 (4) ◽  
pp. 53-65
Author(s):  
Lydia Weiland ◽  
Felix Hanser ◽  
Ansgar Scherp
Keyword(s):  
Search Engine ◽  
Web Search ◽  
User Study ◽  
Quantitative Information ◽  
Multimedia Content ◽  
Structured Interviews ◽  
Web Search Engine ◽  
Multimedia Search ◽  
Multimedia Search And Retrieval ◽  
Search And Retrieval

The authors investigate user requirements regarding the interface design for semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image or video search engines, they are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, the authors conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information. As a result, the authors elicit requirements to a web search engine for multimedia content. Among them, scalability and personalization of the presented information are identified as the main goals. Based on the requirements, they present mockups demonstrating the user interface of a future multimedia search and retrieval engine.

Requirements to a Search Engine for Semantic Multimedia Content

2018 ◽  
pp. 720-734
Author(s):  
Lydia Weiland ◽  
Felix Hanser ◽  
Ansgar Scherp
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Quantitative Information ◽  
Multimedia Content ◽  
Structured Interviews ◽  
Web Search Engine ◽  
Multimedia Search ◽  
Multimedia Search And Retrieval ◽  
Retrieval Engine ◽  
Search And Retrieval

The authors investigate user requirements regarding the interface design for semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image or video search engines, they are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, the authors conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information. As a result, the authors elicit requirements to a web search engine for multimedia content. Among them, scalability and personalization of the presented information are identified as the main goals. Based on the requirements, they present mockups demonstrating the user interface of a future multimedia search and retrieval engine.

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

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