Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.

中文翻译：

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环形空间金字塔映射和基于特征融合的图像编码表示与分类

传统的图像分类模型通常采用单个特征向量来表示信息内容。然而，单个图像特征系统几乎不能提取图像中包含的全部信息，并且传统的编码方法具有很大的特征信息损失。为了解决这个问题，本文提出了一种基于特征融合的图像分类模型。该模型结合了主成分分析（PCA）算法，已处理的尺度不变特征变换（P-SIFT）和颜色命名（CN）特征，以生成相互独立的图像表示因子。在尺度不变特征变换（SIFT）特征的编码阶段，视觉袋词模型（BOVW）用于特征重建。同时，为了将空间信息引入我们提取的特征中，引入旋转不变空间金字塔映射方法进行P-SIFT和CN特征的划分与表示。在特征融合阶段，我们采用带有两个内核的支持向量机（SVM-2K）算法，将训练过程分为两个阶段，最后从相应的内核矩阵中学习知识，以提高分类性能。实验表明，该方法可以有效提高图像描述的准确性和图像分类的准确性。将训练过程分为两个阶段，最后从相应的核矩阵中学习知识，以提高分类性能。实验表明，该方法可以有效提高图像描述的准确性和图像分类的准确性。将训练过程分为两个阶段，最后从相应的核矩阵中学习知识，以提高分类性能。实验表明，该方法可以有效提高图像描述的准确性和图像分类的准确性。