Learning Multifunctional Binary Codes for
Both Category and Attribute Oriented Retrieval Tasks
Abstract
In this paper we propose a unified framework to address
multiple realistic image retrieval tasks concerning both category and attributes. Considering the scale of modern
datasets, hashing is favorable for its low complexity. However, most existing hashing methods are designed to preserve one single kind of similarity, thus incapable of dealing with the different tasks simultaneously. To overcome this
limitation, we propose a new hashing method, named Dual
Purpose Hashing (DPH), which jointly preserves the category and attribute similarities by exploiting the convolutional networks (CNN) to hierarchically capture the correlations between category and attributes. Since images with
both category and attribute labels are scarce, our method is
designed to take the abundant partially labelled images on
the Internet as training inputs. With such a framework, the
binary codes of new-coming images can be readily obtained
by quantizing the network outputs of a binary-like layer, and
the attributes can be recovered from the codes easily. Experiments on two large-scale datasets show that our dual
purpose hash codes can achieve comparable or even better
performance than those state-of-the-art methods specifically designed for each individual retrieval task, while being
more compact than the compared methods