Year: 2005
Type: conference-paper
Abstract: We examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.
Institutions: Carnegie Mellon University, Bilkent University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: NetMine: Finding Patterns in Network Data, CNPq: IMiMD-Indexing and Data Mining in Multimedia Databases, U.S.-Japan Joint Seminar: A Data Mining-Based Approach to Semantic Integration of Stream Data, ITR: Providing Intuitive Access to Human Motion Databases, SENSORS: Placement and Operation of an Environmental Sensor Network to Facilitate Decision Making Regarding Drinking Water Quality and Security, DLI Phase 2: Informedia-II: Integrated Video Information Extraction and Synthesis for Adaptive Presentation and Summarization from Distributed Libraries, Information Technology Research (ITR): Next-Generation Bio-Molecular Imaging and Information Discovery
OpenAlex ID: W2168322495