CAJ | 학술논문

针对单一特征的体育视频分类的正确率低和稳定性差等缺陷，提出一种最小二乘支持向量机（LSSVM）和证据理论相融合的体育视频分类模型（DS-LSSVM）。提取颜色、纹理、亮度、运动矢量场等4种反映体育视频类别特征，将4种单特征的LSSVM初步分类结果作为独立证据构造基本概率指派，运用DS组合规则进行决策级融合，根据分类判决门限给出最终的体育视频分类结果，最后进行仿真实验。结果表明，DS-LSSVM的体育视频分类正确率高达97.90%，相对于参比模型，DS-LSSVM具有体育视频分类正确率高、稳定性好等优势。
침대단일특정적체육시빈분류적정학솔저화은정성차등결함，제출일충최소이승지지향량궤（LSSVM）화증거이론상융합적체육시빈분류모형（DS-LSSVM）。제취안색、문리、량도、운동시량장등4충반영체육시빈유별특정，장4충단특정적LSSVM초보분류결과작위독립증거구조기본개솔지파，운용DS조합규칙진행결책급융합，근거분류판결문한급출최종적체육시빈분류결과，최후진행방진실험。결과표명，DS-LSSVM적체육시빈분류정학솔고체97.90%，상대우삼비모형，DS-LSSVM구유체육시빈분류정학솔고、은정성호등우세。
The correct rate of sports video classification for single feature is very low and stability is poor, this paper proposes a sports video classification method combining Least Squares Support Vector Machine(LSSVM)with evidence theory(DS-LSSVM). The color, texture, brightness, motion vector features of sports video are extracted, and then the extracted features are input into LSSVM to learn and get the preliminary classification results which are taken as evidence to establish the basic probability assignment, and DS is used to decide level fusion, the final sports video classification results are got according to the classifica-tion threshold, the simulation experiment is carried out. The simulation results show that the classification rate of the proposed algorithm reaches 97.90%, compared with the reference algorithms, the proposed algorithm has high video classification rate and good stability advantages.