计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
9期
147-151
,共5页
物体识别%证据理论%极限学习机%尺度不变特征变换%颜色直方图
物體識彆%證據理論%極限學習機%呎度不變特徵變換%顏色直方圖
물체식별%증거이론%겁한학습궤%척도불변특정변환%안색직방도
object recognition%evidence theory%extreme learning machine%scale invariant feature transform%color histogram
为了提高物体的识别正确率，提出一种基于证据理论融合多特征的物体识别算法。提取物体图像的颜色直方图和尺度不变特征，采用极限学习机建立相应的图像分类器，根据单一特征的识别结果构建概率分配函数，并采用证据理论对单一特征识别结果进行融合，得出物体的最终识别结果，采用多个图像数据库对算法有效性进行测试。测试结果表明，该算法不仅提高了物体的识别率，而且加快了物体识别的速度，具有一定的实际应用价值。
為瞭提高物體的識彆正確率，提齣一種基于證據理論融閤多特徵的物體識彆算法。提取物體圖像的顏色直方圖和呎度不變特徵，採用極限學習機建立相應的圖像分類器，根據單一特徵的識彆結果構建概率分配函數，併採用證據理論對單一特徵識彆結果進行融閤，得齣物體的最終識彆結果，採用多箇圖像數據庫對算法有效性進行測試。測試結果錶明，該算法不僅提高瞭物體的識彆率，而且加快瞭物體識彆的速度，具有一定的實際應用價值。
위료제고물체적식별정학솔，제출일충기우증거이론융합다특정적물체식별산법。제취물체도상적안색직방도화척도불변특정，채용겁한학습궤건립상응적도상분류기，근거단일특정적식별결과구건개솔분배함수，병채용증거이론대단일특정식별결과진행융합，득출물체적최종식별결과，채용다개도상수거고대산법유효성진행측시。측시결과표명，해산법불부제고료물체적식별솔，이차가쾌료물체식별적속도，구유일정적실제응용개치。
In order to obtain better recognition results, a novel object recognition method based on multi-feature fusion of evidence theory is proposed. Color histogram and scale invariant feature transform features are extracted from object image, and extreme learning machine is used to establish the classifier;the recognition results of single feature are fused to obtain the last recognition results of object based on evidence theory;the performance of algorithm is tested by some image data. The result illustrates that the proposed algorithm has improved the recognition rate and speed, and it has some application vale.
