Histograms of Sparse Codes for Object Detection
2013-10-11 16:48
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最近在看Histograms of Sparse Codes for Object Detection这篇文章,作者先是对风靡一时,大行其道的hog特征进行了一番狂轰乱炸,说hog特征仅是对图像梯度的充分利用,但是图像内容不仅仅是梯度,还有其他一些文理,边缘,等特性为hog所忽略。hog特征的4种归一化,9个方向,明显的人有意为之的特性,不直观且无吸引力。更重要的是hog特征范化比较困难.(are difficult
to generalize or expand to novel domains (such as depth images or the time-domain),)。
They are gradient-based and lack the ability to directly represent richer (and larger)patterns。There are multiple ad-hoc designs,such as 4-way normalization and 9 orientations, that are non-intuitive and unappealing。
正所谓先破后立,没有比人家强的东西拿出来一味的贬损他人,非君子所为。作者使用K-SVD字典学习法得到图像的稀疏表达,然后将得到的稀疏矩阵模仿hog的方法做成直方图。将得到的一种新的特征称之为HSC,这使得HSC继承了hog的优点,弥补了hog的缺点。
利用HSC特征替代hog进行分类器训练,目标检测识别。实验部分用了root-only 和DPM方法来进行,二者结果比hog特征有明显改善,但速度有所减缓。
to generalize or expand to novel domains (such as depth images or the time-domain),)。
They are gradient-based and lack the ability to directly represent richer (and larger)patterns。There are multiple ad-hoc designs,such as 4-way normalization and 9 orientations, that are non-intuitive and unappealing。
正所谓先破后立,没有比人家强的东西拿出来一味的贬损他人,非君子所为。作者使用K-SVD字典学习法得到图像的稀疏表达,然后将得到的稀疏矩阵模仿hog的方法做成直方图。将得到的一种新的特征称之为HSC,这使得HSC继承了hog的优点,弥补了hog的缺点。
利用HSC特征替代hog进行分类器训练,目标检测识别。实验部分用了root-only 和DPM方法来进行,二者结果比hog特征有明显改善,但速度有所减缓。
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