Bag of Visual Words(BoVW) is the main solution in the current image classification field, whereas the synonymity and ambiguity of the visual words restrict the semantic expression ability of the model and reduce the accuracy of image classification. Aiming to the problem, an adaptive soft assignment method is proposed. Firstly, it analyzes the distance of the Scale Invariant Feature Transform(SIFT) features mapping to visual words, classifies these SIFT features according to certain rules, and applies adaptive allocation strategies to SIFT features with different fuzziness. Then, this paper analyzes the correlations between visual words and image categories via Chi-square model, and then removes the Visual Stop Words(VSW) and reconstructs the histograms. Finally, the images are classified by Support Vector Machine(SVM). The experimental results show that, the method can effectively reduce the impact of the visual words synonymity and ambiguity, and enhance the distinction of visual words, so as to improve the image classification accuracy.%视觉词袋模型(BoVW)是当前图像分类领域的主流方法，然而，视觉单词同义性和歧义性问题严重制约了该模型的性能，进而降低图像分类准确率。针对该问题，本文提出一种基于自适应软分配的图像分类方法。该方法首先对尺度不变特征变换(SIFT)特征映射到视觉单词的距离进行分析，按一定的规则进行归类，并针对具有不同模糊程度的 SIFT特征采用自适应的分配策略；然后，通过卡方模型分析各个视觉单词与图像类别之间的相关性，并依此去除视觉停用词(VSW)，重构视觉单词统计直方图；最后，输入到支持向量机(SVM)完成分类。实验结果表明，该优化方法能有效地降低视觉单词同义性和歧义性问题带来的影响，增强视觉单词的区分性，进而提高图像分类准确率。
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