基于改进的有效区域基因选择与跨模态语义挖掘的图像属性标注

Image Attribute Annotation via a Modified Effective Range Based Gene Selection and Cross-Modal Semantics Mining

图像属性标注是一种更细化的图像标注,它能缩小认知与特征间“语义鸿沟”.现有研究多基于单特征且未挖掘属性蕴含的深层语义,故无法准确刻画图像内容.改进有效区域基因选择算法融合图像特征,并设计迁移学习策略,实现材质属性标注;基于判别相关分析挖掘特征间跨模态语义,以改进相对属性模型,标注材质属性蕴含的深层语义-实用属性.实验表明:材质属性标注精准度达63.11%,较最强基线提升1.97%;实用属性标注精准度达59.15%,较最强基线提升2.85%;层次化的标注结果能全面刻画图像内容.

Image attribute annotation is a refined method of image annotation.It can narrow the“semantic gap”between cognition and features.However,a single feature is used to characterize images and the deep-level semantics are not fully explored.So annotations cannot depict images comprehensively.The traditional effective range based gene selection algorithm is modified to complete feature fusion.And transfer learning strategy is designed to complete material annotation.The cross-modal semantics among features are mined by the discriminant correlation analysis algorithm.So the relative attribute model is optimized to complete deep-level semantics(practical attributes)annotation.Experimental results demonstrate:Material attributes annotation accuracy reaches 63.11%,which is improved by 1.97%compared with baseline.Practical attributes annotation accuracy reaches 59.15%,which is improved by 2.85%compared with baseline.The proposed hierarchical annotation mechanism can more comprehensively depict images.