中文摘要：

商品图像检索的目标是检索与图像内容相符的商品,它是移动视觉搜索在电子商务中的重要应用.商品图像检索的发展,既为用户购物提供便利,又促进了电子商务向移动端发展.图像特征是影响商品图片检索性能的重要因素.复杂的图片背景、同类商品之间的相似性和被拍摄商品尺度的变化,都使得商品图像检索对图像特征提出了更高的要求.提出了一种多尺度深度神经网络,以便于抽取对复杂图片背景和目标物体尺度变化更加鲁棒的图像特征.同时根据商品类别标注信息学习图片之间的相似度.针对在线服务对响应速度的要求,通过压缩模型的深度和宽度控制了计算开销.在一个百万级的商品图片数据集上的对比实验证明：该方法在保持速度的同时提升了查询的准确率.

英文摘要：

Product image search is an important application of mobile visual search in e-commerce.The target of product image search is to retrieve the exact product in a query image.The development of product image search not only facilitates people＇s shopping,but also results in that e-commerce moves forward to mobile users.As one of the most important performance factors in product image search,image representation suffers from complicated image background,small variance within each product category,and variant scale of the target object.To deal with complicated background and variant object scale,we present a multi-scale deep model for extracting image representation.Meanwhile,we learn image similarity from product category annotations. We also optimize the computation cost by reducing the width and depth of our model to meet the speed requirements of online search services.Experimental results on a million-scale product image dataset shows that our method improves retrieval accuracy while keeps good computation efficiency,comparing with existing methods.