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Building a Compact Relevant Sample Coverage for Relevance Feedback in Content-Based Image
 

Summary: Building a Compact Relevant Sample Coverage
for Relevance Feedback in Content-Based Image
Retrieval
Bangpeng Yao1
, Haizhou Ai1
, and Shihong Lao2
1
Computer Science & Technology Department, Tsinghua University, Beijing, China
2
Sensing & Control Technology Laboratory, Omron Corporation, Kyoto, Japan
Abstract. Conventional approaches to relevance feedback in content-
based image retrieval are based on the assumption that relevant images
are physically close to the query image, or the query regions can be
identified by a set of clustering centers. However, semantically related
images are often scattered across the visual space. It is not always reli-
able that the refined query point or the clustering centers are capable of
representing a complex query region.
In this work, we propose a novel relevance feedback approach which
directly aims at extracting a set of samples to represent the query re-
gion, regardless of its underlying shape. The sample set extracted by

  

Source: Ai, Haizhou - Department of Computer Science and Technology, Tsinghua University
Li, Fei-Fei - Department of Computer Science, Stanford University

 

Collections: Computer Technologies and Information Sciences; Engineering