toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author (up) Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny edit   pdf
doi  isbn
  Title Leveraging category-level labels for instance-level image retrieval Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3045-3052  
  Abstract In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results.  
  Address Providence, Rhode Island  
  Corporate Author Thesis  
  Publisher IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ GRP2012 Serial 2050  
Permanent link to this record
Select All    Deselect All
 |   | 

Save Citations:
Export Records: