基于多源异质特征的紧致二进制编码学习

为了提高图像检索系统的精度，提出了一种基于多种异质特征的新颖哈希函数学习方法．该方法首先利用特征空间中相似样本与非相似样本分布的不平衡性来提升每个弱分类器的性能，从而建立非对称的Boosting框架；然后将一种基于异质特征子空间学习的线性判别弱分类器融入该框架下，并利用每轮算法中的误判样本的信息来依次学习紧致且平衡的哈希编码．该方法能有效地融合具有互补功能的不同模态的信息，实现了检索系统的性能提升．在2个公开数据集上的实验结果表明该方法优于其他算法，由此看出增加多源异质特征和利用不平衡性学习紧致哈希编码都可以大大提高图像检索的精度．

Learning compact binary code based on multiple heterogeneous features

A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.