Visual Keywords: from Text Retrieval to Multimedia Retrieval

2000 ◽  
pp. 77-101 ◽  
Author(s):  
Joo-Hwee Lim
Keyword(s):  
Text Retrieval ◽  
Multimedia Retrieval ◽  
Visual Keywords

Design and Evaluation of a Content-Based Image Retrieval System

2011 ◽  
pp. 125-151 ◽  
Author(s):  
David Squire ◽  
Henning Muller ◽  
Wolfgang Muller ◽  
Stephane Marchand-Maillet ◽  
Thierry Pun
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Understanding ◽  
Text Retrieval ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Context Dependency ◽  
Content Based Image Retrieval ◽  
Classical Text ◽  
Image Retrieval System

The growth in size and accessibility of multimedia databases has changed our approach to information retrieval. Classical text-based systems show their limitations in the context of multimedia retrieval. In this chapter, we address the problem of conceiving and evaluating a content-based image retrieval system. First, we investigate the use of the query-by-example (QBE) paradigm as a base paradigm for the development of a content-based image retrieval system (CBIRS). We show that it should be considered as a complement to the classical textual-based paradigms. We then evaluate the capabilities of the most up-to-date computer vision techniques in contributing to the realisation of such a system. Further, beyond the necessity of accurate image understanding techniques, we show that the amount of the data involved in the process of describing image content should also be considered as an important issue. This aspect of our study is largely based on the experience acquired by the text retrieval (TR) community, which we adapt to the context of CBIR. Similarly, the text retrieval community has also developed significant experience in evaluating retrieval systems, where judgements include subjectivity and context dependency. Extending this experience, we study a coherent framework for performing the evaluation of a CBIRS. As a practical example, we user our Viper CBIR system, using a novel communication protocol called MRML (Multimedia Retrieval Markup Language) to pinpoint the importance of the sharing of resources in facilitating the evaluation and therefore the development of CBIRS.

Design and Evaluation of a Content-Based Image Retrieval System

2011 ◽  
pp. 38-72
Author(s):  
David Squire ◽  
Henning Muller ◽  
Wolfgang Muller ◽  
Stephane Marchand-Maillet ◽  
Thierry Pun
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Understanding ◽  
Text Retrieval ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Context Dependency ◽  
Content Based Image Retrieval ◽  
Classical Text ◽  
Image Retrieval System

The growth in size and accessibility of multimedia databases has changed our approach to information retrieval. Classical text-based systems show their limitations in the context of multimedia retrieval. In this chapter, we address the problem of conceiving and evaluating a content-based image retrieval system. First, we investigate the use of the query-by-example (QBE) paradigm as a base paradigm for the development of a content-based image retrieval system (CBIRS). We show that it should be considered as a complement to the classical textual-based paradigms. We then evaluate the capabilities of the most up-to-date computer vision techniques in contributing to the realisation of such a system. Further, beyond the necessity of accurate image understanding techniques, we show that the amount of data involved in the process of describing image content should also be considered as an important issue. This aspect of our study is largely based on the experience acquired by the text retrieval (TR) community, which we adapt to the context of CBIR. Similarly, the text retrieval community has also developed significant experience in evaluating retrieval systems, where judgements include subjectivity and context dependency. Extending this experience, we study a coherent framework for performing the evaluation of a CBIRS. As a practical example, we use our Viper CBIR system, using a novel communication protocol called MRML (Multimedia Retrieval Markup Language) to pinpoint the importance of the sharing of resources in facilitating the evaluation and therefore the development of CBIRS.

scholarly journals A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1274
Author(s):  
Daniel Bonet-Solà ◽  
Rosa Ma Alsina-Pagès
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Multimedia Retrieval ◽  
Natural Environments ◽  
K Nearest Neighbor ◽  
Acoustic Environments

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.

Semantic-Preserving Metric Learning for Video-Text Retrieval

2021 ◽  
Author(s):  
Sungkwon Choo ◽  
Seong Jong Ha ◽  
Joonsoo Lee
Keyword(s):  
Metric Learning ◽  
Text Retrieval

scholarly journals Using generative probabilistic models for multimedia retrieval

2005 ◽  
Vol 39 (1) ◽  
pp. 69-69 ◽  
Author(s):  
Thijs Westerveld
Keyword(s):  
Probabilistic Models ◽  
Multimedia Retrieval
Sign in / Sign up

Export Citation Format

Share Document