Content-Based Image Retrieval Using Deep Search

被引：0

作者：

Zhou, Zhengzhong ^{[1
]}

Zhang, Liqing ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Key Lab Shanghai Educ Commiss Intelligent Interac, Shanghai, Peoples R China

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II | 2016年 / 9948卷

关键词：

CBIR; Deep search; Image semantics;

D O I：

10.1007/978-3-319-46672-9_70

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of Content-based Image Retrieval (CBIR) is to find a set of images that best match the query based on visual features. Most existing CBIR systems find similar images in low level features, while Text-based Image Retrieval (TBIR) systems find images with relevant tags regardless of contents in the images. Generally, people are more interested in images with similarity both in contours and high-level concepts. Therefore, we propose a new strategy called Deep Search to meet this requirement. It mines knowledge from the similar images of original queries, in order to compensate for the missing information in feature extraction process. To evaluate the performance of Deep Search approach, we apply this method to three different CBIR systems (HOF [5], HOG and GIST) in our experiments. The results show that Deep Search greatly improves the performance of original algorithms, and is not restricted to any particular methods.

引用

页码：627 / 634

页数：8