一种改进半监督学习的图像语义自动识别方法

Image Semantic Automatic Identification Method Based on An Improved Semi-Supervised Learning

针对大量不相关的冗余特征制约图像关键特征分类性,导致语义识别模型出现偏差的问题,提出一种混合约束的半监督图像语义特征选择方法。在半监督学习的基础上,算法采用成对约束和数据清洗相结合的特征评价方法快速去除不相关图像干扰特征,聚类过程中考虑可转换语义直接的特征相关性,保证语义特征正确。实验结果表明,与传统的语义特征识别方法相比,该方法能以更少的特征获得更好的语义特征分类性能,有更好的识别效果。

Because the analysis of a large amount of irrelevant and redundant features restrict image key characteristics classification the semantic identification model appeared deviation,the paprer proposes a kind of hybrid constraint and semi-supervised image semantic feature selection method.Based on a semi-supervised learning the methods combined constraints in pairs and data cleaning to rapid removal irrelevant uses evaruation method which characteristics,clustring process consider convertible semantic direct correlation characteristics,ensure the correct semantic features.The experi-mentar results show that,with the traditionar semantic feature recognition method,this method can compared with less features get better semantic feature classification performance,have a better recognition effect.