CAJ | 학술논문

针对视觉显著性分析不能辨别目标且单个特征描述目标具有局限性的问题，提出基于视觉显著性及多特征分析的目标检测。首先，对已标定训练图，生成遍历整幅图像的随机采样区域，通过多特征分析获取每个区域包含目标可能性的先验参数信息；然后，对测试图，依据上述先验信息，基于贝叶斯模型计算每个随机采样区域包含目标可能性的评分值，并将值高的若干区域标记为目标候选区域；最后，结合显著性分析及判别准则，对候选区域进一步判定，以确定最大可能涵盖目标的区域，从而实现目标检测。研究结果表明：显著性分析具有对目标所在区域的主动选择性；多特征结合能有效描述目标以使目标更具可区分性。
침대시각현저성분석불능변별목표차단개특정묘술목표구유국한성적문제，제출기우시각현저성급다특정분석적목표검측。수선，대이표정훈련도，생성편력정폭도상적수궤채양구역，통과다특정분석획취매개구역포함목표가능성적선험삼수신식；연후，대측시도，의거상술선험신식，기우패협사모형계산매개수궤채양구역포함목표가능성적평분치，병장치고적약간구역표기위목표후선구역；최후，결합현저성분석급판별준칙，대후선구역진일보판정，이학정최대가능함개목표적구역，종이실현목표검측。연구결과표명：현저성분석구유대목표소재구역적주동선택성；다특정결합능유효묘술목표이사목표경구가구분성。
Due to the difficulty to indicate an object for the existent visual saliency models and the limitation of representation ability with single feature ,an algorithm of object extraction was projected combined with multi‐feature and visual saliency analysis .Firstly ,for the train images ,the random windows uniformly distribu‐ted over the entire image were sampled and the prior information of parameters were learned through the analy‐sis of multi‐feature .Secondly ,for the test image ,the random windows were sampled too and their scores were calculated integrated the analysis of characteristics above in Bayes Model .Finally ,the regions output by this method were arranged according to their weights and the region most possibly contained object was determined combined with the analysis of saliency and its criterion ,thus the target detection was achieved ultimately .The results showed that the algorithm has an active selectivity to the candidate regions depended on the analysis of visual saliency and has a distinguish ability between target and non‐target due to the combination of multi‐fea‐ture .