Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Year: 2013
Type: preprint
Abstract: Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries. Recently further developed Deep Neural Network sheds light on automatically learning high-level image representation from raw pixels. In this paper, we proposed a multi-task DNN learned for image retrieval, which contains two parts, i.e., query-sharing layers for image representation computation and query-specific layers for relevance estimation. The weights of multi-task DNN are learned on clickthrough data by Ring Training. Experimental results on both simulated and real dataset show the effectiveness of the proposed method.
Source: arXiv (Cornell University)
Authors: Bai, Yalong, Yang, Kuiyuan, Yu, Wei, Ma, Wei-Ying, Zhao, Tiejun
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2294339199