电子学报2017，Vol.45Issue(6)：1462-1468,7.DOI:10.3969/j.issn.0372-2112.2017.06.026
基于混合属性的零样本图像分类
Hybrid Attribute-Based Zero-Shot Image Classification
摘要
Abstract
When the dimensionality of the semantic attributes is limited,it is difficult for attribute-based zero-shot image classifiers to distinguish the objects with similar attributes.Aiming at the limitation of describing objects with semantic attributes,an improved direct attribute prediction(DAP) model for zero-shot image classifying based on hybrid attribute(HA) is proposed,which is called HA-DAP.At first,we carry out the sparse coding on the low-level features to obtain the non-semantic attributes that are used to assist the existing semantic attributes.Then,we take the hybrid attributes including the learned non-semantic attributes and the manually specified semantic attributes as the mid-layer of DAP model and use the idea of attribute prediction to train the hybrid attribute-based classifier.At last,according to the predicted hybrid attributes and the relationship between the attributes and classes,we can recognize the class label for the testing sample.Experimental results on the OSR,Pub Fig and Shoes datasets show that,the HA-DAP outperforms the DAP in the classification performance,i.e.,when compared with the DAP,the proposed HA-DAP yields much higher zero-shot image classification accuracy and AUC value.关键词
零样本图像分类/混合属性/语义属性/非语义属性/稀疏编码Key words
zero-shot image classification/hybrid attribute/semantic attribute/non-semantic attribute/sparse auto-encoding分类
信息技术与安全科学引用本文复制引用
程玉虎,乔雪,王雪松..基于混合属性的零样本图像分类[J].电子学报,2017,45(6):1462-1468,7.基金项目
国家自然科学基金(No.61472424,No.61273143) （No.61472424,No.61273143）
中央高校基本科研业务费(No.2013RC10,No.2013RC12,No.2014YC07) （No.2013RC10,No.2013RC12,No.2014YC07）
