scholarly journals Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

2013 ◽  
Vol 28 (4) ◽  
pp. 351-367 ◽  
Author(s):  
Michalis Lazaridis ◽  
Apostolos Axenopoulos ◽  
Dimitrios Rafailidis ◽  
Petros Daras
Keyword(s):  
Indexing Techniques ◽  
Multimedia Search ◽  
Multimedia Search And Retrieval ◽  
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.

Multimedia Search and Retrieval

2000 ◽  
Author(s):  
Shih-Fu Chang ◽  
Qian Huang ◽  
Thomas Huang ◽  
Atul Puri ◽  
Behzad Shahraray
Keyword(s):  
Multimedia Search ◽  
Multimedia Search And Retrieval ◽  
Search And Retrieval

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.

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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