CAJ | 학술논문

基于未确知测度理论，提出了一种研究视频情感内容的新算法，建立起视频高层语义与底层特征间的联系。首先，选取能反映视频场景情感变化的视觉类与音频类底层特征，并藉此构建了视频场景对象的视觉类情感特征向量与音频类情感特征向量。其次，分析视频场景情感内容度量的对象空间、指标空间与情感判定空间，确立了视觉、音频情感特征指标的未确测度函数，并建立相应的未确知测度矩阵。最后，采用信息熵权法对各底层情感指标赋值，根据置信度识别准则对视频场景的情感类型进行识别、判定。实验结果验证了该方法的可行性和有效性。
기우미학지측도이론，제출료일충연구시빈정감내용적신산법，건립기시빈고층어의여저층특정간적련계。수선，선취능반영시빈장경정감변화적시각류여음빈류저층특정，병자차구건료시빈장경대상적시각류정감특정향량여음빈류정감특정향량。기차，분석시빈장경정감내용도량적대상공간、지표공간여정감판정공간，학립료시각、음빈정감특정지표적미학측도함수，병건립상응적미학지측도구진。최후，채용신식적권법대각저층정감지표부치，근거치신도식별준칙대시빈장경적정감류형진행식별、판정。실험결과험증료해방법적가행성화유효성。
In this paper,a novel algorithm based on the unascertained measure theory is proposed to study the affective contents which are usually contained in video scenes.In order to implement the semantic recognition,several models have been established between the low-level features and the high-level cognitive emotion in video scene. Firstly,on the one hand the low-level visual features named as scene light,shot cut rate and color energy are specially selected as the measure parameters to construct the visual emotion feature vector.On the other hand,five audio com-prehensive features are eventually obtained by dimension reduce,and the auditory emotion feature vector is created accordingly.The visual and auditory features in video scenes are highlighted because of their capabilities to distinguish video affective semantic.Secondly,the unascertained obj ect space is moderately built to formulize each video scene.After that,the emotion index space is consequentially constructed so that a series of unascertained measure functions are respectively formed to quantify the each low-level feature.Furthermore,the emotional decision space is reasonably designed to bridge the gap between the unascertained obj ect space and the emotion index space. Then,the specific process of building the measure matrix is discussed by means of the unascertained measure theory in detail.Finally,the information entropy is adopted to determine the coefficients of weight in the unascertained measurement models.According to the results from above analysis,the degree of confidence concept is introduced to evaluate which emotion type could exactly match the affective content in the target video scene.A series of the contrast experiments were carefully performed to test the robust performance of the method.The experimental results verify the feasibility and effectiveness of the proposed algorithm.