Leveraging category-level labels for instance-level image retrieval

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 experime...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Albert Gordo, José A. Rodríguez-Serrano, Florent Perronnin, Ernest Valveny
• Keywords: Computer Vision, Cvpr 2012, Dimensional Representations, Dimensionality Reduction, Retrieval Accuracy