CAJ | 학술논문

在笔式用户界面中,对手绘图形和手写文字的识别通常采用不同的识别算法,因此通过笔画分类将混杂的笔画集自动分离是手绘草图识别中的一个重要研究课题.该文提出一种融合时空上下文的手绘笔画联合分类方法,采用支持向量随机场对时空关联的笔画集进行联合建模,不仅利用笔画自身的特征进行判别分类,还以时空邻域和笔画对特征同时融合了笔画间的时间上下文和空间上下文信息,通过模型环状置信传播(LBP)推断,最终求得最大后验边缘概率准则下的联合分类结果.实验结果表明,该文方法的分类准确率优于基于SVM的单笔画分类方法和基于马尔科夫随机场(MRF)的空间上下文联合分类方法,分类速度能基本满足交互实时性的要求,验证了利用随机场模型融合时空上下文进行笔画分类的可行性和有效性.
재필식용호계면중,대수회도형화수사문자적식별통상채용불동적식별산법,인차통과필화분류장혼잡적필화집자동분리시수회초도식별중적일개중요연구과제.해문제출일충융합시공상하문적수회필화연합분류방법,채용지지향량수궤장대시공관련적필화집진행연합건모,불부이용필화자신적특정진행판별분류,환이시공린역화필화대특정동시융합료필화간적시간상하문화공간상하문신식,통과모형배상치신전파(LBP)추단,최종구득최대후험변연개솔준칙하적연합분류결과.실험결과표명,해문방법적분류준학솔우우기우SVM적단필화분류방법화기우마이과부수궤장(MRF)적공간상하문연합분류방법,분류속도능기본만족교호실시성적요구,험증료이용수궤장모형융합시공상하문진행필화분류적가행성화유효성.
Most pen-based user interfaces are incapable of recognizing both graphical symbols and text with a single recognizer. Thus, it is essential to distinguish between graphical strokes and textual ones before feeding them into the appropriate recognizer. An approach for classifying sketched strokes is presented using Support Vector Random Field (SVRF). Inputting strokes as well as the interactions among them are jointly modeled by the random field. Not only the unary features of strokes themselves are utilized for discriminative classification, but also their temporal and spatial context are exploited through neighborhood system and features of binary stroke pairs. After applying Loopy Belief Propagation (LBP) inferring, the joint labeling solution according to maximum posterior marginal criterion is estimated. Experimental results show that the classification accuracy of the approach outperforms the Support Vector Machine (SVM) classifier as well as the Markov Random Field (MRF)-based joint classification approach which utilizes spatial context. The speed of classification meets basically the requirement of real-time interaction. Thus the feasibility and effectiveness of the proposed approach are verified.