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Multifeature Analysis and Semantic Context Learning for Image Classification
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DOI：10.1145/2457450.2457454.png)
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Abstract
En 中文
This article introduces an image classification approach in which the semantic context of images and multiple low-level visual features are jointly exploited. The context consists of a set of semantic terms defining the classes to be associated to unclassified images. Initially, a multiobjective optimization technique is used to define a multifeature fusion model for each semantic class. Then, a Bayesian learning procedure is applied to derive a context model representing relationships among semantic classes. Finally, this context model is used to infer object classes within images. Selected results from a comprehensive experimental evaluation are reported to show the effectiveness of the proposed approaches.
Keywords:
Algorithms
Image classification
object detection
multifeature fusion
semantic context modeling
Journal
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2.0K
Citations:
5.4K