CAJ | 학술논문

局部特征和全局特征是图像的两种重要的特征描述，在图像分类时起着至关重要的作用。据此提出一种通过融合全局与局部特征核量化图像分类方法。首先，分析全局特征及局部特征各自优缺点，并对图像进行特征提取；其次，通过核方法将特征映射到适当的高维空间中，来进行码书的获取与量化，并进行特征的融合以更好地对图像进行描述；最后，采用基于直方图交叉核的支持向量机对获取的量化特征进行分类。通过实验证明了所提出的方法的可行性。
국부특정화전국특정시도상적량충중요적특정묘술，재도상분류시기착지관중요적작용。거차제출일충통과융합전국여국부특정핵양화도상분류방법。수선，분석전국특정급국부특정각자우결점，병대도상진행특정제취；기차，통과핵방법장특정영사도괄당적고유공간중，래진행마서적획취여양화，병진행특정적융합이경호지대도상진행묘술；최후，채용기우직방도교차핵적지지향량궤대획취적양화특정진행분류。통과실험증명료소제출적방법적가행성。
Local feature descriptor and global feature descriptor are two significant descriptors of image, which play crucial role in image classification.Based on this, we propose an image classification method using kernel quantification for fusing global and local features.First, the respective pros and cons of global feature and local feature are analysed and used to extract the image features.Secondly, the features are mapped onto appropriate high dimension space through kernel approach to get the codebook and to do quantification, and the features fusion is carried out for better description on the image.Lastly, the SVM based on histogram intersection kernel is employed to classify the quantified features.It is proved through experiment the feasibility of the method proposed.