计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2015年
4期
232-235,240
,共5页
局部Gist特征%梯度方向直方图%特征组合%场景描述%支持向量机%场景分类
跼部Gist特徵%梯度方嚮直方圖%特徵組閤%場景描述%支持嚮量機%場景分類
국부Gist특정%제도방향직방도%특정조합%장경묘술%지지향량궤%장경분류
local Gist feature%Histograms of Oriented Gradient( HOG)%feature combination%scene description%Support Vector Machine( SVM)%scene classification
局部Gist方法提取的特征维数过高、计算复杂，单一的Gist特征不能很好地描述全局场景。为此，提出一种将改进的局部Gist特征与梯度方向直方图特征进行组合的场景描述方法。采用支持向量机作为分类器，在WS场景库中考察单一特征和组合特征的分类精度，在OT场景库下研究不同数量训练样本对于分类精度的影响。实验结果表明，与全局Gist、局部Gist等方法相比，该方法能降低计算的复杂度，且提高分类正确率。
跼部Gist方法提取的特徵維數過高、計算複雜，單一的Gist特徵不能很好地描述全跼場景。為此，提齣一種將改進的跼部Gist特徵與梯度方嚮直方圖特徵進行組閤的場景描述方法。採用支持嚮量機作為分類器，在WS場景庫中攷察單一特徵和組閤特徵的分類精度，在OT場景庫下研究不同數量訓練樣本對于分類精度的影響。實驗結果錶明，與全跼Gist、跼部Gist等方法相比，該方法能降低計算的複雜度，且提高分類正確率。
국부Gist방법제취적특정유수과고、계산복잡，단일적Gist특정불능흔호지묘술전국장경。위차，제출일충장개진적국부Gist특정여제도방향직방도특정진행조합적장경묘술방법。채용지지향량궤작위분류기，재WS장경고중고찰단일특정화조합특정적분류정도，재OT장경고하연구불동수량훈련양본대우분류정도적영향。실험결과표명，여전국Gist、국부Gist등방법상비，해방법능강저계산적복잡도，차제고분류정학솔。
In view of complex computation caused by extracting high dimension characteristics with local Gist method, as well as the problem that the sole Gist characteristic can not describe global scenes well,a kind of improved method to describe the scenes is proposed,which combines local Gist characteristics with Histograms of Oriented Gradient( HOG) characteristics . Classification accuracy of the sole characteristics and the combination of characteristics are inspected in the WS scene database using Support Vector Machine( SVM) as the classifier. On this basis,classified precision influenced by different quantity training samples is also studied in the OT scenes database. Experimental results show that this method reduces the computational complexity,and improves the classified accuracy compared with the global Gist,local Gist methods,etc.
