Deep Learning for Content-Based Image Retrieval
Chinese Academy of Sciences · Institute of Computing Technology · +3 more institutions
Abstract
Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known ``semantic gap'' issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning…
Citation impact
- FWCI
- 51.65
- Percentile
- 100%
- References
- 62
Authors
7Topics & keywords
- Semantic gap
- Computer science
- Deep learning
- Image retrieval
- Artificial intelligence
- Convolutional neural network
- Content-based image retrieval
- Machine learning
- Quality Education