scholarly journals Leveraging Image, Text and Cross–media Similarities for Diversity–focused Multimedia Retrieval

2010 ◽  
pp. 315-342 ◽  
Author(s):  
Julien Ah-Pine ◽  
Stephane Clinchant ◽  
Gabriela Csurka ◽  
Florent Perronnin ◽  
Jean-Michel Renders
Keyword(s):  
Multimedia Retrieval ◽  
Cross Media

Media-mix japonés: funcionamiento y posibilidades

Neuróptica ◽  
2020 ◽  
pp. 249-252
Author(s):  
Julia Rigual Mur
Keyword(s):  
Cross Media ◽  
Media Mix

Reseña del libro: HERNÁNDEZ PÉREZ, M., Manga, anime y videojuegos. Narrativa cross-media japonesa, Zaragoza, Prensas Universitarias de Zaragoza, 2017.

Cross-Media Retrieval Method Based on Content Correlations

2009 ◽  
Vol 31 (5) ◽  
pp. 820-826 ◽  
Author(s):  
Hong ZHANG ◽  
Fei WU ◽  
Yue-Ting ZHUANG ◽  
Jian-Xun CHEN
Keyword(s):  
Retrieval Method ◽  
Cross Media

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.

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