Robinson, J: Support Vector Machine Learning
-
- Deutsch ausgewählt
68,00 €
inkl. gesetzl. MwSt.,
Lieferung nach Hause
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
28.11.2008
Verlag
VDMSeitenzahl
176
Maße (L/B/H)
22/15/1,1 cm
Gewicht
250 g
Sprache
Deutsch
ISBN
978-3-639-10000-6
Methods exploring the application of support vector
machine learning (SVM) to still image compression are
detailed in both the spatial and frequency domains.
In particular the sparse properties of SVM learning
are exploited in the compression algorithms. A
classic radial basis function neural network requires
that the topology of the network be defined before
training. An SVM has the property that it will choose
the minimum number of training points to use as
centres of the Gaussian kernel functions. It is this
property that is exploited as the basis for image
compression algorithms presented in this book.
Several novel algorithms are developed applying SVM
learning to both directly model the colour surface
and model transform coefficients after the surface
has been transformed into the frequency domain. It is
demonstrated that compression is more efficient in
frequency space.
In the frequency domain, results are superior to that
of JPEG. For example, the quality of the industry
standard 'Lena' image compressed 63:1 for JPEG is
slightly worse quality than the same image compressed
192:1 with the RKi-1 algorithm detailed in this book.
machine learning (SVM) to still image compression are
detailed in both the spatial and frequency domains.
In particular the sparse properties of SVM learning
are exploited in the compression algorithms. A
classic radial basis function neural network requires
that the topology of the network be defined before
training. An SVM has the property that it will choose
the minimum number of training points to use as
centres of the Gaussian kernel functions. It is this
property that is exploited as the basis for image
compression algorithms presented in this book.
Several novel algorithms are developed applying SVM
learning to both directly model the colour surface
and model transform coefficients after the surface
has been transformed into the frequency domain. It is
demonstrated that compression is more efficient in
frequency space.
In the frequency domain, results are superior to that
of JPEG. For example, the quality of the industry
standard 'Lena' image compressed 63:1 for JPEG is
slightly worse quality than the same image compressed
192:1 with the RKi-1 algorithm detailed in this book.
Kundinnen und Kunden meinen
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kund*innen durch Ihre Meinung
Kurze Frage zu unserer Seite
Vielen Dank für dein Feedback
Wir nutzen dein Feedback, um unsere Produktseiten zu verbessern. Bitte habe Verständnis, dass wir dir keine Rückmeldung geben können. Falls du Kontakt mit uns aufnehmen möchtest, kannst du dich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice