基于深度学习耦合稀疏语义度量的商标检索算法

Trademark Retrieval Algorithm Based on Deep Learning Coupled Sparse Semantic Measure

目的针对当前商标图像检索中的语义鸿沟问题,提出一种深度学习耦合稀疏语义度量的商标图像检索方案,有效抑制噪声干扰,降低冗余特征维数。方法首先,根据由卷积与池化组成的无监督学习机制,对输入商标图像进行多层特征提取,输出一维特征向量。随后,通过L2-支持向量机(L2-SVM)进行分类,利用特征向量进行训练,获得多级联特征。然后,根据商标图像的多级联特征和用户标签信息的异构数据结构,设计一种稀疏语义度量方法进行相似检索,减少语义鸿沟。此外,引入一种混合范数作为相似度量的稀疏约束,以抑制原始输入空间中的冗余特征维数和噪声,优化检索结果。结果实验表明,与当前流行的商标检索方案相比,所提算法具有更高的检索精度,其输出的结果中仅有1幅无关图像。结论该方案具有较高的检索精度和较强的鲁棒性,在商标检测、商标保护等方面中具有良好的应用价值。

The work aims to propose a trademark image retrieval scheme with deep learning coupled sparse semantic measure to effectively restrain noise interference and reduce redundant feature dimension,with respect to the problem of semantic gap in trademark image retrieval.Firstly,according to the unsupervised learning mechanism composed of convolution and cistern,the multi-layer feature extraction of input trademark image were carried out to output the one-dimensional feature vector.Then,the L2-support vector machine(L2-SVM)was used to classify the feature vectors,and the multilevel features were obtained based on the training with feature vectors.Then,according to the multilevel feature of the trademark image and the heterogeneous data structure of the user's label information,a sparse semantic measure method was designed for similar retrieval,which reduced the semantic gap.In addition,a mixed norm was introduced as a sparse constraint of similarity measure to suppress the redundant feature dimension and noise in the original input space and optimize the retrieval results.The experiment showed that,compared with the current popular trademark retrieval scheme,the proposed algorithm had higher retrieval accuracy,whose output results only had one irrelevant image.The proposed scheme has higher retrieval precision and stronger robustness,and it has good application value in trademark detection,trademark protection and so on.