论文标题
CBIR使用深度学习得出的功能
CBIR using features derived by Deep Learning
论文作者
论文摘要
在基于内容的图像检索（CBIR）系统中，任务是从给定查询图像的大数据库中检索类似的图像。通常的过程是从查询图像中提取一些有用的功能，并检索具有相似功能集的图像。为此，选择了合适的相似性度量，并检索了具有高相似性得分的图像。自然，这些功能的选择在该系统的成功中起着非常重要的作用，并且需要高级功能来减少语义差距。 在本文中，我们建议使用从训练大图像分类问题的深度学习卷积网络中获得的预训练网络模型的功能。这种方法似乎为各种数据库产生了极好的成果，并且优于许多当代CBIR系统。我们分析了该方法的检索时间，并根据上述特征提出了数据库的预簇，该特征在大多数情况下在更短的时间内产生可比的结果。
In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the semantic gap. In this paper, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases.
