结合自适应融合网络与哈希的图像检索算法

卷积神经网络因其对图像识别准确率高而在图像检索领域备受青睐,但处理大规模数据集时,基于卷积神经网络提取的深度特征维度高,容易引发"维度灾难".针对图像检索中深度特征维度高的问题,提出一种基于自适应融合网络特征提取与哈希特征降维的图像检索算法.由于传统哈希处理高维特征复杂度高,因此本文在卷积神经网络中加入自适应融合模块对特征进行重新整合,增强特征表征能力的同时降低特征维度;然后应用稀疏化优化算法对深度特征进行第2次降维,并通过映射获得精简的哈希码;最后,实验以Inception网络作为基础模型,在数据集CIFAR-10和ImageNet上进行了丰富的实验.实验结果表明,该算法能有效提高图像检索效率.

A novel image retrieval algorithm with adaptive fusion network and hash

Because of its high accuracy in image recognition, convolutional neural network is very popular in the field of image retrieval. However, in the face of large datasets, the high deep feature dimension extracted by convolutional neural network is likely to cause “the curse of dimensionality”. Aiming at the problem of the high dimension of deep features in image retrieval, a novel image retrieval algorithm based on adaptive fusion network feature extraction with hash feature reduction is proposed. Due to the high complexity of high-dimensional features in traditional hash processing, an adaptive fusion module is added to the convolutional neural network to re-integrate the features, to enhance the capability of feature representation and reduce the feature dimension. Then, the deep feature dimension is reduced by feature sparsity optimization algorithm for the second time, and the reduced hashing code is obtained by mapping. Finally, using Inception network as the basic model, a number of experiments were conducted on CIFAR-10 and ImageNet datasets. Experimental results show that this algorithm can effectively improve the efficiency of image retrieval.