论文标题
通过分析深神经网络层的激活来生成描述符的新方法来检索图像。
A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers
论文作者
论文摘要
在本文中，我们考虑了使用深神经网络基于内容的图像检索任务的描述符构建问题。基于完全连接的层激活的神经代码的概念通过合并卷积层中包含的信息来扩展。众所周知，网络卷积部分中神经元的总数很大，大多数对最终分类决策的影响很小。因此，在本文中，我们提出了一种新型算法，该算法使我们能够提取最重要的神经元激活并利用这些信息来构建有效的描述符。由从完全连接和卷积层获得的值组成的描述符完美地表示整个图像内容。使用这些描述符检索的图像在语义上非常匹配与查询图像，并且它们在其他次要图像特征（例如背景，纹理或颜色分布）中也相似。使用VGG16神经网络根据Imagenet1M数据集对所提出的描述符的这些特征进行实验验证。
In this paper, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers activations, is extended by incorporating the information contained in convolutional layers. It is known that the total number of neurons in the convolutional part of the network is large and the majority of them have little influence on the final classification decision. Therefore, in the paper we propose a novel algorithm that allows us to extract the most significant neuron activations and utilize this information to construct effective descriptors. The descriptors consisting of values taken from both the fully connected and convolutional layers perfectly represent the whole image content. The images retrieved using these descriptors match semantically very well to the query image, and also they are similar in other secondary image characteristics, like background, textures or color distribution. These features of the proposed descriptors are verified experimentally based on the IMAGENET1M dataset using the VGG16 neural network.
