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    解冰, 朱宏擎. 一种基于选择性卷积特征和最大后验高斯混合模型的细粒度图像分类算法[J]. 华东理工大学学报(自然科学版), 2019, 45(5): 789-794. DOI: 10.14135/j.cnki.1006-3080.20180603001
    引用本文: 解冰, 朱宏擎. 一种基于选择性卷积特征和最大后验高斯混合模型的细粒度图像分类算法[J]. 华东理工大学学报(自然科学版), 2019, 45(5): 789-794. DOI: 10.14135/j.cnki.1006-3080.20180603001
    XIE Bing, ZHU Hongqing. Fine Grained Image Classification Using Low Dimensional Selective Convolutional Descriptors and GMM Mean-Only MAP Adaption[J]. Journal of East China University of Science and Technology, 2019, 45(5): 789-794. DOI: 10.14135/j.cnki.1006-3080.20180603001
    Citation: XIE Bing, ZHU Hongqing. Fine Grained Image Classification Using Low Dimensional Selective Convolutional Descriptors and GMM Mean-Only MAP Adaption[J]. Journal of East China University of Science and Technology, 2019, 45(5): 789-794. DOI: 10.14135/j.cnki.1006-3080.20180603001

    一种基于选择性卷积特征和最大后验高斯混合模型的细粒度图像分类算法

    Fine Grained Image Classification Using Low Dimensional Selective Convolutional Descriptors and GMM Mean-Only MAP Adaption

    • 摘要: 提出了一种新颖的细粒度图像分类算法。首先从神经网络VGG 16中提取出卷积特征后进行特征筛选,得到选择性卷积特征;然后利用最大后验高斯混合模型对特征进行分类,从而解决细粒度图像分类问题。造成细粒度图像分类困难的主要原因是类内差异和类间差异。利用卷积特征对图像具有更细致的描述能力,可以有效地减小类内差异;同时,对从VGG 16中得到的卷积特征进行筛选,能够较大程度地摆脱背景干扰,从而提高类间差异。最后,采用基于最大后验的高斯混合模型对这些选择性卷积特征进行分类。实验结果表明,本文算法不仅克服了两种差异带来的问题,还解决了传统高斯混合模型缺少大量实验数据的困难。在目前流行的5种细粒度图像数据集上,本文算法都有更好的分类效果。

       

      Abstract: Fine grained image classification is a challenging task in computer vision, whose aim is to recognize images that belong to the same basic category but not the same class or subcategory. This image classification is intractable for two reasons. The first one is that the images of the same class obtained from real world contain different environment, illumination, and postures. These differences will result in high intra-class variances. The other is that some classes under the same basic category look very similar to each other, which results in low inter-class differences. Traditional fine grained image classification approaches divide the input image into many overlapping patches. Thus, these patches may contain huge amount of redundant information. Local region descriptors extracted from these patches will require a lot of computation time. Recently, convolutional neural network (CNN) has been widely used in fine grained images classification and has shown its effectiveness when dealing with large amount of image. However, it is usually difficult for CNN to obtain the qualified annotations. Although the bounding box regression may reduce the influence of lacking annotations, it may inevitably contain background or noisy parts. Hence, this paper proposes a selective convolutional descriptor with mean only maximum a posterior adaption via GMM (SCD-MGMM), which can effectively deal with the shortcoming of intra-class and inter-class variances. Firstly, the convolutional features are chosen from entire images by using SCD approach so that the features extracted from a pertained VGG 16 model has stronger robustness to noise. Thus, the proposed SCD-MGMM can automatically locate the main object without any supervised information or extra fine grained images training. Besides, the proposed framework utilizes the mean only maximum a posterior adaption based on GMM (MGMM) to overcome the shortcoming that GMM requires a lot of observation data. Finally, this paper adopts a fast linear scoring technique to compute the log-likelihood. It has been shown from quantitative experiment results that the proposed method can attain better fine grained classification.

       

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