计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
20期
153-156,182
,共5页
加速稳健特征(SURF)%图像分割%图像分类%自学习%主动轮廊模型
加速穩健特徵(SURF)%圖像分割%圖像分類%自學習%主動輪廊模型
가속은건특정(SURF)%도상분할%도상분류%자학습%주동륜랑모형
Speeded Up Robust Features(SURF)%image segmentation%image classification%self learning%active con-tour model
为解决图像分类过程中特征点选择的随机性对分类精度造成的影响，提出一种基于图像目标特征空间自学习分类算法。利用基于颜色和纹理特征的多通道局部主动轮廊模型找到图像的目标区域，在目标区域选取特征并对特征稀疏编码建立图像的目标特征空间。为进一步提高图像分类精度建立投票机制下基于图像目标特征空间的自学习算法。实验结果表明，该方法能避免特征选择的随机性对实验结果的影响，有效地提高图像分类的精度。
為解決圖像分類過程中特徵點選擇的隨機性對分類精度造成的影響，提齣一種基于圖像目標特徵空間自學習分類算法。利用基于顏色和紋理特徵的多通道跼部主動輪廊模型找到圖像的目標區域，在目標區域選取特徵併對特徵稀疏編碼建立圖像的目標特徵空間。為進一步提高圖像分類精度建立投票機製下基于圖像目標特徵空間的自學習算法。實驗結果錶明，該方法能避免特徵選擇的隨機性對實驗結果的影響，有效地提高圖像分類的精度。
위해결도상분류과정중특정점선택적수궤성대분류정도조성적영향，제출일충기우도상목표특정공간자학습분류산법。이용기우안색화문리특정적다통도국부주동륜랑모형조도도상적목표구역，재목표구역선취특정병대특정희소편마건립도상적목표특정공간。위진일보제고도상분류정도건립투표궤제하기우도상목표특정공간적자학습산법。실험결과표명，해방법능피면특정선택적수궤성대실험결과적영향，유효지제고도상분류적정도。
In the image classification process, the classification accuracy is affected by the randomicity of feature point selec-tion. In order to solve this problem, a self-learning classification algorithm based on image object feature space is pro-posed. The image target area is found with the local multi-channel active contour model based on the colour and texture feature. Features are selected in the target area and it makes sparse coding on features, which is used to establish the fea-ture space. In order to further improve the classification accuracy, a voting mechanism is established on the proposed algo-rithm. Experimental results indicate that the proposed algorithm can avoid the impact of the randomicity of feature point selection, and effectively improve the accuracy of image classification.
