一种粒子群优化融合特征的零样本图像分类算法

Zero-Shot Image Classification Algorithm Based on Particle Swarm Optimization Fusion Feature

针对目标类语义属性描述的局限性,提出一种基于自适应加权融合特征的零样本图像分类算法。首先,随机初始化融合权重,利用神经网络融合文本的语义词向量特征和语义属性;然后,利用粒子群算法优化特征融合的权重;最后,把加权融合的特征作为零样本图像分类的迁移知识。实验结果表明,基于自适应加权融合的零样本图像分类算法在动物属性数据集(AWA)上测试的准确率达到88.9%,验证了该方法的有效性。同时与融合特征算法相比,亦提高了零样本图像分类模型的稳定性。

Aiming at the limitation of describing the semantic attributes of target classification,this paper proposes an adaptive weighted fusion feature based zero-sampling image classification algorithm.Firstly,the fusion weights are initialized randomly.Meantime,the semantic vector features and semantic attributes of the text are fused by neural network.Then,particle swarm optimization algorithm is used to optimize the weight of feature fusion.Finally,the features of weighted fusion are regarded as the transfer knowledge of the classification of zero-sampling images.The experimental results show that the classification algorithm based on adaptive weighted fusion for the zero-sampling image has an accuracy rate of 88.9%on the Animals with Attributes(AWA)data set,which illustrates the effectiveness.What's more,the proposed algorithm also improves the stability of the classification model for the zero-sampling image compared with the fusion feature.