CAJ | 학술논문

常见的物体识别算法是基于图像局部低维特征的，在图像成像质量较差、分辨率较低情况下存在不确定和歧义性；图像上下文包含了场景信息以及物体之间彼此关联的丰富信息，可作为图像低维特征补充从而有助于提高物体识别率。该文在已有上下文模型基础上，进一步考虑了物体的空间位置关系信息，将图像全局特征、物体同现性关系和空间位置关系信息、局部检测器输出整合到同一个概率框架中，并充分利用树结构图模型高效推理的优势，改进了物体识别性能。最后通过标准图像集进行算法验证测试和对比来说明该文算法的有效性。
상견적물체식별산법시기우도상국부저유특정적，재도상성상질량교차、분변솔교저정황하존재불학정화기의성；도상상하문포함료장경신식이급물체지간피차관련적봉부신식，가작위도상저유특정보충종이유조우제고물체식별솔。해문재이유상하문모형기출상，진일보고필료물체적공간위치관계신식，장도상전국특정、물체동현성관계화공간위치관계신식、국부검측기수출정합도동일개개솔광가중，병충분이용수결구도모형고효추리적우세，개진료물체식별성능。최후통과표준도상집진행산법험증측시화대비래설명해문산법적유효성。
Most object recognition algorithms are based on the local low-dimensional characteristics of the images.Images of poor quality or low resolution prompt uncertainty and ambiguity for object recognition .Context information contains scenes and spatial relationships information among objects which can be used as the supplementary for low-dimensional image features to improve the recognition .In this paper , we present an improved context model for object recognition in which we further consider the spatial relationships .We integrate all the information in a unified probabilistic framework taking full advantage of the efficient inference algorithms of tree models .Our model can improve the recognition result and give a consistent scene interpretation of the image.At last, we use a standard dataset to test our algorithm and make comparison to show the effectiveness .