排序方式: 共有1条查询结果,搜索用时 15 毫秒
1
1.
For the task of visual-based automatic product image classification for e-commerce, this paper constructs a set of support
vector machine (SVM) classifiers with different model representations. Each base SVM classifier is trained with either different
types of features or different spatial levels. The probability outputs of these SVM classifiers are concatenated into feature
vectors for training another SVM classifier with a Gaussian radial basis function (RBF) kernel. This scheme achieves state-of-the-art
average accuracy of 86.9% for product image classification on the public product dataset PI 100. 相似文献
1