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Erschienen in:
Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU) Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Discriminative Feature Learning with an Optimal Pattern Model for Image Classification

verfasst von : Lijuan Liu, Yu Bao, Haojie Li, Xin Fan, Zhongxuan Luo

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

The co-occurrence features learned through pattern mining methods have more discriminative power to separate images from other categories than individual low-level features. However, the “pattern explosion” problem involved in mining process prevents its application in many visual tasks. In this paper, we propose a novel scheme to learn discriminative features based on a mined optimal pattern model. The proposed method deals with the “pattern explosion” problem from two aspects, (1) it uses selected weak semantic patches instead of grid patches to substantially reduce the database to mine; (2) the adopted optimal pattern model can produce compact and representative patterns which make the resulted image code more effective and discriminative for classification. In our work, we apply the minimal description length (MDL) to mine the optimal pattern model. We evaluate the proposed method on two publicly available datasets (15-Scenes and Oxford-Flowers17) and the experimental results demonstrate its effectiveness.

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Vorheriges Kapitel Depth Map Coding by Modeling the Locality and Local Correlation of View Synthesis Distortion in 3-D Video
Nächstes Kapitel Sign Language Recognition Based on Trajectory Modeling with HMMs
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Metadaten
Titel
Discriminative Feature Learning with an Optimal Pattern Model for Image Classification
verfasst von
Lijuan Liu
Yu Bao
Haojie Li
Xin Fan
Zhongxuan Luo
Copyright-Jahr
2016
Buch
MultiMedia Modeling

Print ISBN: 978-3-319-27670-0

Electronic ISBN: 978-3-319-27671-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-27671-7_57

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