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中文摘要：
已有的针对上下文信息的大多数工作均侧重于视觉词之间的上下文信息建模，没有考虑到局部特征之间的上下文信息建模问题，且图像在拍照时往往受到姿势、尺度变化，光照以及相机参数的影响，导致分类精度不高．文中综合考虑局部特征之间的上下文信息，提出一种基于有判别力仿射局部特征上下文的图像分类方法．对于一幅图像上的某一位置，采用该区域的局部特征，及其周边一定距离、角度内的局部特征来进行描述（局部特征上下文）；然后对这些局部特征上下文进行仿射变换，并通过最小化编码损失的策略来进行有判别力的仿射局部特征上下文的选择，得到更有判别力的特征．最后通过实验结果验证了该方法的有效性．
英文摘要：
Most of context based methods focus on using context information at the visual word level without considering the relationship between local features. Besides, images are often captured with various poses, scale changes, illumination variation and camera parameters. This hinders the improvement of image classification performance. By combining contextual information of local features, this paper proposes a novel discriminative affine local feature context method for efficient image classification. We use the local feature at the position as well as other local features based on their distances and angels to this position. Affine transformations are done to the local feature context in order to get more robust and effective features. The discriminative affine-transformed local feature context is then chosen by minimizing the reconstruction error. Classification experiments demonstrate the effectiveness of the proposed method.
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