图 书 馆
图像检索中的稀疏编码特征Sparse-Coded Features for Image Retrieval |
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| 课程网址: | http://videolectures.net/bmvc2013_ge_image_retrieval/ |
| 主讲教师: | Tiezheng Ge |
| 开课单位: | 中国科学技术大学 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  在本文中，我们应用稀疏编码来得出紧凑型仍然可以从多种类型的特征中区分图像，以进行大规模图像检索。我们首先将每个特征描述符转换为稀疏代码，然后通过最大池化将每种类型的稀疏编码特征聚合为单个向量。然后，将来自不同类型特征的多个矢量连接起来并进行压缩以获得最终表示。我们的方法使我们可以添加更多类型的功能，以在不牺牲可伸缩性的情况下提高可分辨性。特别是，我们设计了一个新的微特征，以补充现有的局部不变特征。通过使用稀疏编码框架将微特征与各种局部不变特征相结合，我们最终的紧凑表示形式在检索性能和可伸缩性方面均优于最新技术。 |
| 课程简介: | State-of-the-art image retrieval systems typically represent an image with a bag of low-level features. Since different images often exhibit different kinds of low-level characteristics, it is desirable to represent an image with multiple types of complementary features. The systems scalability is, however, significantly lowered when increasing the number of feature types, as the amount of data is also increased rapidly both in index and in query representation. In this paper, we apply sparse coding to derive a compact yet discriminative image representation from multiple types of features for large-scale image retrieval. We first convert each feature descriptor into a sparse code, and aggregate each type of sparsecoded features into a single vector by max-pooling. Multiple vectors from different types of features are then concatenated and compressed to obtain the final representation. Our approach allows us to add more types of features to improve discriminability without sacrificing scalability. In particular, we design a new micro feature which is complementary to existing local invariant features. By combining our micro feature with various local invariant features using the sparse-coding framework, our final compact representation outperforms the state of the art both in retrieval performance and in scalability. |
| 关 键 词: | 图像检索; 矢量连接 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-30：zyk |
| 最后编审: | 2021-04-09：yumf |
| 阅读次数: | 31 |