scholarly journals Some Experiments in Evaluating ASR Systems Applied to Multimedia Retrieval

2011 ◽  
pp. 12-23 ◽  
Author(s):  
Julián Moreno ◽  
Marta Garrote ◽  
Paloma Martínez ◽  
José L. Martínez-Fernández
Keyword(s):  
Multimedia Retrieval

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.

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

Multimedia retrieval at INEX 2006

2007 ◽  
Vol 41 (1) ◽  
pp. 58-63 ◽  
Author(s):  
Thijs Westerveld ◽  
Roelof van Zwol
Keyword(s):  
Multimedia Retrieval

Semantic-driven Interpretable Deep Multi-modal Hashing for Large-scale Multimedia Retrieval

2020 ◽  
pp. 1-1
Author(s):  
Xu Lu ◽  
Li Liu ◽  
Liqiang Nie ◽  
Xiaojun Chang ◽  
Huaxiang Zhang
Keyword(s):  
Large Scale ◽  
Multimedia Retrieval

scholarly journals Maximum Variance Hashing via Column Generation

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Lei Luo ◽  
Chao Zhang ◽  
Yongrui Qin ◽  
Chunyuan Zhang
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Web Search ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Multimedia Retrieval ◽  
Training Data ◽  
Nonlinear Dimensionality Reduction ◽  
Maximum Variance ◽  
Data Volume

With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search. Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing. Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work. The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data. To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions. We then extend it using anchor graphs to reduce the computational cost. Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.

Semantic Boosting Cross-Modal Hashing for efficient multimedia retrieval

2016 ◽  
Vol 330 ◽  
pp. 199-210 ◽  
Author(s):  
Ke Wang ◽  
Jun Tang ◽  
Nian Wang ◽  
Ling Shao
Keyword(s):  
Multimedia Retrieval
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