毕业生知识库

2. 提出了一种多尺度全卷积的图像实例检索方法

目前已有很多工作利用卷积神经网络提取图像特征进行图像检索，但是这 些工作并未对影响图像特征有效性的各种因素进行详细分析，例如，图像尺寸 缩放的策略，影响多尺度特征有效性的因素等，因此各种因素如何影响检索的 性能仍不明确。在该工作中，我们对输入神经网络的图像尺寸缩放策略，提取 图像多尺度特征的方式，以及 PCA 和白化矩阵学习这三个重要的因素进行了研 究，通过实验分析了这些因素对检索结果的影响。在此基础上，我们提出了多 尺度全卷积的图像特征提取方法。该方法简单而有效，我们在Oxford5k,Paris6k, Oxford105k以及UKB这四个常用数据库上进行了实验，大量的实验结果表明我 们提出的方法有着良好的检索效果。

 

Content-based image retrieval is an important and traditional research topic in computer vision. It has also been widely applied in industrial applications. In recent years, deep learning methods have been very popular. Due to the excellent ability of the convolutional neural networks to represent an image, approaches based on it have achieved remarkable sucess over the traditional methods in areas such as image classification, image retrieval, object detection and semantic segmentation. Although it has been studied for a long time, image retrieval still faces a lot of challenges, for example, the large variation of object scale, pose and lighting conditions in different images will affect the performance of retrieval methods significantly. In this paper, we mainly study image retrieval based on deep convolutional neural networks and its application on sensitive image retrieval. The work and contributions of this paper are summarized below:
 

3. An approach for fine-grained sensitive object retrieval based on double margin contrastive loss

 

There are great needs for automatically regulating shocking firearm images in social media or identifying firearm types in forensic science. Image retrieval techniques have a great potential to solve such problems. Recent advances in image retrieval are mainly driven by fine-tuning state-of-the-art convolutional neural networks for retrieval task. The contrastive loss, known for its simplicity and good performance, has been widely used. We find that it performs poorly for the Firearm14k dataset due to: (1) Loss contributed by similar and dis-similar image pairs during training is unbalanced. (2) A huge domain gap exists between this dataset and ImageNet. We propose to deal with the unbalanced loss by employing a double margin contrastive loss. We tackle the domain gap issue with a two-stage training strategy, where we first fine-tune the network for classification and then fine-tune it for retrieval. Extensive experiments show that our approach outperforms the state-of-the-art methods on fine-grained firearm image retrieval task.