一种用于图像分类的语义增强线性编码方法

针对传统编码模型中存在的编码歧义性问题，该文提出一种考虑特征上下文的语义增强线性编码方法。首先，通过学习局部邻域中特征共生关系矩阵来表示上下文信息。然后，在编码过程中同时引入学习而得的上下文信息与特征上下文匹配权重得到语义增强编码模型。由于上下文信息与上下文匹配权重的功能，使得此编码方法不仅丰富了编码的语义信息，还能够有效避免噪声带来的影响。在3个基准数据集(Scene15, Caltech101以及 Caltech256)上充分的实验验证了该方法的有效性。

A Semantic Enhanced Linear Coding for Image Classification

Considering the ambiguity problem in the traditional feature coding model, a feature context-aware semantic enhanced linear coding method is proposed. At first, the context information is represented by the concurrence matrix learnt from local area of the features. Then, the context information and a context matching weight are introduced into the coding model to form a new semantic enhanced coding model. Owning to the functions of context information and the context matching weight, this model not only enriches the semantic meaning of coding, but also efficiently avoids the affects of noise. Experiments on the baselines (Scene15, Caltech101, and Caltech256) demonstrate the effectiveness of the proposed method.