Information Fusion in Multimedia Information Retrieval

Kludas, Jana; Bruno, Éric; Marchand-Maillet, Stéphane

doi:10.1007/978-3-540-79860-6_12

Cited by 54 publications

(43 citation statements)

References 24 publications

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“…A fusion method should be able to recognize the part of information that is reliable [22]. Different disciplines such as signal and image processing [23], data mining [24], Forex trading [25], etc.…”

Section: Fusion For Multimodal Biometric Systemsmentioning

confidence: 99%

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Talebi

Gavrilova

2015

Computer Analysis of Images and Patterns

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In recent years, the inevitable need for reliable biometric identity management systems in applications such as border crossing, welfare distribution, and accessing critical facilities has drawn researchers' attention to the area of biometric. The intrinsic limitations of unimodal biometric systems such as non-universality, sensitivity to noisy sensor data, inter and intra class variations and spoof attacks have resulted in significant attention toward multimodal biometric systems. An important aspect of a multimodal biometric system is the fusion of information from multiple biometric sources. This thesis focuses on using the notion of Resemblance Probability Distributions to calculate confidence measures for different biometric matchers and use these confidence measures in the fusion module to improve the identification rate of the system. This thesis approaches the problem of low inter class variation and low quality data by proposing Rank List Reinforcement and Confidence-based Ranked List Selection methods.

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Section: Fusion For Multimodal Biometric Systemsmentioning

confidence: 99%

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Talebi

Gavrilova

2015

Computer Analysis of Images and Patterns

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“…Data/feature level fusion combines the original data or extracted features through certain fusion strategies. One major drawback of fusion at this level is due to the problem of "curse of dimensionality", which is usually computationally expensive and requires a large set of training data [24]. Furthermore, the fusion problem may be difficult…”

Section: Multimodal Information Fusionmentioning

confidence: 99%

Multimodal Information Retrieval: Challenges and Future Trends

UbaidullahBokhari¹,

Hasan²

2013

IJCA

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Multimodal information retrieval is a research problem of great interest in all domains, due to the huge collections of multimedia data available in different contexts like text, image, audio and video. Researchers are trying to incorporate multimodal information retrieval using machine learning, support vector machines, neural network and neuroscience etc. to provide an efficient retrieval system that fulfills user need. This paper is an overview of multimodal information retrieval, challenges in the progress of multimodal information retrieval. General TermsMulti Modal Information Retrieval, Information Retrieval.

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“…Information fusion is a relevant and important research field with many applications, such as face recognition, multimodal biometrics, image analysis, and multimedia information retrieval [1][2][3][4]. A follow-up work [5] studies the fusion problems in a more theoretical way.…”

Section: Introductionmentioning

confidence: 99%

Fusion and inference from multiple data sources in a commensurate space

Marchette

Priebe

2012

Statistical Analysis

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Given objects measured under multiple conditions-for example, indoor lighting versus outdoor lighting for face recognition, multiple language translation for document matching, etc.-the challenging task is to perform data fusion and utilize all the available information for inferential purposes. We consider two exploitation tasks: (i) how to determine whether a set of feature vectors represent a single object measured under different conditions; and (ii) how to create a classifier based on training data from one condition in order to classify objects measured under other conditions. The key to both problems is to transform data from multiple conditions into one commensurate space, where the (transformed) feature vectors are comparable and would be treated as if they were collected under the same condition. Toward this end, we studied Procrustes analysis and developed a new approach, which uses the interpoint dissimilarities for each condition. We impute the dissimilarities between measurements of different conditions to create one omnibus dissimilarity matrix, which is then embedded into Euclidean space. We illustrate our methodology on English and French documents collected from Wikipedia, demonstrating superior performance compared to that obtained via standard Procrustes transformation. 

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Information Fusion in Multimedia Information Retrieval

Cited by 54 publications

References 24 publications

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Multimodal Information Retrieval: Challenges and Future Trends

Fusion and inference from multiple data sources in a commensurate space

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