CAJ | 학술논문

传统的Gabor滤波器具有良好的方向特性和尺度特性,然而传统的Gabor滤波器不能提取图像中弯曲区域的局部信息。文中首先对传统的Gabor滤波器加以改进,使其在具有方向和尺度特性的同时具有良好的曲率响应特性,因而对于图像中弯曲的区域能够提取丰富的边缘特征。图像在不同的Gabor滤波器特征下有不同的表现形式,利用Gabor滤波器丰富的多特征信息,可以形成包含丰富信息的多个模态。然后文中提出一个多模态学习( Multi-modal Learning)框架。在此框架内,样本集合被投影到一个公共的鉴别空间内,在这个空间里,来自不同模态的同类样本相互聚集,异类样本相互散开。文中提出的多模态学习框架能很好地利用Gabor滤波器的多特征信息,PolyU掌纹数据库和AR彩色人脸数据库的实验结果表明了该方法的有效性。
전통적Gabor려파기구유량호적방향특성화척도특성,연이전통적Gabor려파기불능제취도상중만곡구역적국부신식。문중수선대전통적Gabor려파기가이개진,사기재구유방향화척도특성적동시구유량호적곡솔향응특성,인이대우도상중만곡적구역능구제취봉부적변연특정。도상재불동적Gabor려파기특정하유불동적표현형식,이용Gabor려파기봉부적다특정신식,가이형성포함봉부신식적다개모태。연후문중제출일개다모태학습( Multi-modal Learning)광가。재차광가내,양본집합피투영도일개공공적감별공간내,재저개공간리,래자불동모태적동류양본상호취집,이류양본상호산개。문중제출적다모태학습광가능흔호지이용Gabor려파기적다특정신식,PolyU장문수거고화AR채색인검수거고적실험결과표명료해방법적유효성。
Traditional Gabor filter has good characteristics of direction and scale,but cannot extract the local information of bending area for image. Firstly,improve traditional Gabor filter to make it has good curvature response based on good characteristics of direction and scale. So for the image area can extract the edge of the rich characteristics of bending. After filtering with different characteristics of Gabor filter,images have more abundant characteristic information,and contain abundant information of multiple modes. Then propose a Multi-Modal Learning ( MML) framework,within this framework,samples are projected onto a common space. In this common space,samples in same class from multiple modals are close to each other,while samples in different classes from multiple modals are far away from each other. Multi-modal learning framework proposed in this paper can make good use of Gabor filter characteristic information. Experimental results with PolyU palmprint database and AR color data set show the effectiveness of the method in this paper.