• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>European conference on computer vision >Neural Codes for Image Retrieval
【24h】

Neural Codes for Image Retrieval

机译：图像检索的神经电图

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

It has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image. In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application. In the experiments with several standard retrieval benchmarks, we establish that neural codes perform competitively even when the convolutional neural network has been trained for an unrelated classification task (e.g. Image-Net). We also evaluate the improvement in the retrieval performance of neural codes, when the network is retrained on a dataset of images that are similar to images encountered at test time. We further evaluate the performance of the compressed neural codes and show that a simple PCA compression provides very good short codes that give state-of-the-art accuracy on a number of datasets. In general, neural codes turn out to be much more resilient to such compression in comparison other state-of-the-art descriptors. Finally, we show that discriminative dimensionality reduction trained on a dataset of pairs of matched photographs improves the performance of PCA-compressed neural codes even further. Overall, our quantitative experiments demonstrate the promise of neural codes as visual descriptors for image retrieval.
机译：已经表明，由大卷积神经网络的顶层内的图像调用的激活提供了图像的视觉内容的高级描述符。在本文中，我们研究了在图像检索应用程序中使用这种描述镜（神经码）。在具有若干标准检索基准的实验中，我们建立了即使当卷积神经网络已经培训过不相关的分类任务（例如图像网）时也表现得很竞争。我们还评估了神经码的检索性能的提高，当网络在类似于在测试时间遇到的图像的图像数据集上掠夺网络时。我们进一步评估了压缩神经电图的性能，并表明简单的PCA压缩提供了非常好的短代码，可在许多数据集上给出最先进的准确性。通常，在比较其他最先进的描述符中，神经码向这些压缩变得更加有弹性。最后，我们表明，在匹配照片对的数据集上训练的判别维度减少改善了PCA压缩神经电图的性能。总体而言，我们的定量实验证明了神经码作为图像检索的视觉描述符的承诺。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号