自动化学报Issue(5)：960-969,10.DOI:10.16383/j.aas.2015.c140238
一种多层次抽象语义决策图像分类方法
A Multiple Layer Abstract Semantic Decision Method for Image Classification
摘要
Abstract
Bag-of-visual-words (BoVW) is an effective method in image categorizing and retrieving task. A multiple layer decision method (MLD), which introduces abstract semantics of image categories into BoVW to carry out middle-level and upper-level extensions, is proposed in this paper. Semantics is preserved at the stage of generating visual vocabulary, based on which classifiers are trained in a bottom-up way. Abstract semantics is transferred during the training step. After that, the category of a test image is estimated gradually by classifier through each layer in a top-down way. Experiments on standard datasets show that the proposed method achieves better performance compared with mainstream classification methods.关键词
图像分类/图像模糊分类/视觉词包模型/决策树/多层次决策Key words
Image classification/image fuzzy classification/bag-of-visual-words (BoVW)/decision tree/multiple layer decision (MLD)引用本文复制引用
刘鹏,叶志鹏,赵巍,唐降龙..一种多层次抽象语义决策图像分类方法[J].自动化学报,2015,(5):960-969,10.基金项目
国家自然科学基金(61171184,61201309,61440025)资助Supported by National Natural Science Foundation of China (61171184,61201309,61440025) （61171184,61201309,61440025）
