基于半监督学习的增量图像分类方法

为有效使用大量未标注的图像进行分类，提出一种基于半监督学习的图像分类方法。通过共同的隐含话题桥接少量已标注的图像和大量未标注的图像，利用已标注图像的Must-link约束和Cannot-link约束提高未标注图像分类的精度。实验结果表明，该方法有效提高Caltech-101数据集和7类图像集约10%的分类精度。此外，针对目前绝大部分半监督图像分类方法不具备增量学习能力这一缺点，提出该方法的增量学习模型。实验结果表明，增量学习模型相比无增量学习模型提高近90%的计算效率。关键词半监督学习，图像分类，增量学习中图法分类号TP391。41IncrementalImageClassificationMethodBasedonSemi-SupervisedLearningLIANGPeng1，2，LIShao-Fa2，QINJiang-Wei2，LUOJian-Gao31(SchoolofComputerScienceandEngineering，GuangdongPolytechnicNormalUniversity，Guangzhou510665)2(SchoolofComputerScienceandEngineering，SouthChinaUniversityofTechnology，Guangzhou510006)3(DepartmentofComputer，GuangdongAIBPolytechnicCollege，Guangzhou510507)ABSTRACTInordertouselargenumbersofunlabeledimageseffectively，animageclassificationmethodisproposedbasedonsemi-supervisedlearning。Theproposedmethodbridgesalargeamountofunlabeledimagesandlimitednumbersoflabeledimagesbyexploitingthecommontopics。Theclassificationaccuracyisimprovedbyusingthemust-linkconstraintandcannot-linkconstraintoflabeledimages。TheexperimentalresultsonCaltech-101and7-classesimagedatasetdemonstratethattheclassificationaccuracyimprovesabout10%bytheproposedmethod。Furthermore，duetothepresentsemi-supervisedimageclassificationmethodslackingofincrementallearningability，anincrementalimplementationofourmethodisproposed。Comparingwithnon-incrementallearningmodelinliterature，theincrementallearningmethodimprovesthecomputationefficiencyofnearly90%。