Abstract
Current Google image search adopt a hybrid search approach in which a text-based query (e.g., "Paris landmarks") is used to retrieve a set of relevant images, which are then refined by the user (e.g., by re-ranking the retrieved images based on similarity to a selected example). We conjecture that given such hybrid image search engines, learning per-query distance functions over image features can improve the estimation of image similarity. We propose scalable solutions to learning query-specific distance functions by 1) adopting a simple large-margin learning framework, 2) using the query-logs of text-based image search engine to train distance functions used in content-based systems. We evaluate the feasibility and efficacy of our proposed system through comprehensive human evaluation, and compare the results with the state-of-the-art image distance function used by Google image search.
Original language | English (US) |
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Article number | 6585834 |
Pages (from-to) | 2022-2034 |
Number of pages | 13 |
Journal | IEEE Transactions on Multimedia |
Volume | 15 |
Issue number | 8 |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Keywords
- Content based retrieval
- Distance learning
- Image processing
- Image search
- Search engine
ASJC Scopus subject areas
- Signal Processing
- Media Technology
- Computer Science Applications
- Electrical and Electronic Engineering