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Learning robust independent bases for accurate scene categorization

机译:学习强大的独立基础以进行准确的场景分类

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Due to the importance of feature extraction and scene representation in classification tasks, this paper presents an approach for unsupervised feature learning using Independent Subspace Analysis. The optimization process of feature bases is incorporated into the framework of incremental learning to cope with the learning difficulty with large or dynamic samples. The proposed method could automatically learn image features and accomplish scene classification with Spatial Pyramid Matching model. Also, the influence of related parameters in optimization and classification is discussed. Experiment shows the proposed method constructs efficient scene description and outperforms several previous methods in classification on OT scene dataset.
机译:由于特征提取和场景表示在分类任务中的重要性,本文提出了一种使用独立子空间分析进行无监督特征学习的方法。特征库的优化过程被合并到增量学习的框架中,以应对大型或动态样本的学习困难。该方法可以自动学习图像特征,并利用空间金字塔匹配模型完成场景分类。此外,还讨论了相关参数对优化和分类的影响。实验表明,该方法在OT场景数据集的分类中,构造了高效的场景描述,并且优于以前的几种方法。

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