CAJ | 학술논문

大规模图像集合的自动分组，不仅可以帮助用户快速组织和掌握图像集合的内容，并且是基于图像的三维场景重建应用的前提和重要环节。提出一种基于词袋模型（bag-of-words ， BOW）的层次化分组算法，将每幅图像表示为一个超高维视词向量，利用多路量化技术将内容相似的图像量化到同一个节点，从而完成对图像粗略分组。然后，在每组类别里面，对图像的局部特征向量进行逐一匹配，并利用仿射空间不变量的约束条件，去除不可靠特征匹配，得到更为准确可靠的图像相似度度量，从而完成图像的精细分组。实验结果表明：从得到的系统不同阶段图像分组的查准率-查全率（precision-recall）曲线可以看出，精细分组过程可以显著提高粗分组精度，并且在精细分组阶段，使用约束条件比不使用约束还能获得更高的分组精度。
대규모도상집합적자동분조，불부가이방조용호쾌속조직화장악도상집합적내용，병차시기우도상적삼유장경중건응용적전제화중요배절。제출일충기우사대모형（bag-of-words ， BOW）적층차화분조산법，장매폭도상표시위일개초고유시사향량，이용다로양화기술장내용상사적도상양화도동일개절점，종이완성대도상조략분조。연후，재매조유별리면，대도상적국부특정향량진행축일필배，병이용방사공간불변량적약속조건，거제불가고특정필배，득도경위준학가고적도상상사도도량，종이완성도상적정세분조。실험결과표명：종득도적계통불동계단도상분조적사준솔-사전솔（precision-recall）곡선가이간출，정세분조과정가이현저제고조분조정도，병차재정세분조계단，사용약속조건비불사용약속환능획득경고적분조정도。
Automatical grouping algorithm on large-scale image set ,which is an important part of the scene reconstruction system ,can help users organize the image set contents quickly .A hierarchical image grouping algorithm based on bag-of-words(BOW) was proposed .Firstly , each image is projected to a super-high dimensional visual word vector by a multiple paths quantization (MPQ) method ,and this step is so-called coarse grouping .Then ,feature matc-hing is carried out in every divided group and an affine invariant constraint is proposed to get rid of the incorrect matching features .This step is so-called refined grouping which can im-prove image grouping accuracy .The precision-recall curves show that the refined grouping can obviously improve the accucy of coase grouping ,and better grouping accucy can be achieved w hen using constraints .