NDLI: New kernels for analyzing multimodal data in multimedia using kernel machines

Content Provider	IEEE Xplore Digital Library
Author	Aradhye, H. Dorai, C.
Copyright Year	2002
Description	Author affiliation: SRI Int., Menlo Park, CA, USA (Aradhye, H.)
Abstract	Research in automated analysis of digital media content has led to a large collection of low-level feature extractors, such as face detectors, videotext extractors, speech and speaker identifiers, people/vehicle trackers, and event locators. These media metadata are often symbolic rather than continuous-valued, and pose significant difficulty to subsequent tasks such as classification and dimensionality reduction which traditionally deal with continuous-valued data. This paper proposes a novel mechanism that extends tasks traditionally limited to continuous-valued feature spaces, such as (a) dimensionality reduction, (b) de-noising, and (c) clustering, to domains with symbolic features. To this end, we introduce new kernels based on well-known distance metrics, and prove Mercer validity of these kernels for analyzing symbolic feature spaces. We demonstrate their usefulness within the context of kernel-space methods such as Kernel PCA and SVM, in classifying machine learning datasets from the UCI repository and in temporal clustering and tracking of videotext in multimedia. We show that the generalized kernels help capture information from symbolic feature spaces, visualize symbolic data, and aid tasks such as classification and clustering, and therefore are useful in multimodal analysis of multimedia.
Starting Page	37
Ending Page	40
File Size	462836
Page Count	4
File Format	PDF
ISBN	0780373049
DOI	10.1109/ICME.2002.1035368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-26
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Data analysis Speech analysis Data mining Face detection Feature extraction Event detection Vehicle detection Detectors Vehicles
Content Type	Text
Resource Type	Article

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