Content-Based Image Retrieval
Yuanbin Wang, Haoran Wang
Outline
▪ Introduction
▪ Data and Models
▪ Architecture
▪ Demo
Content-Based Image Retrieval
Introduction
▪ Image retrieval
▪ retrieve images given an query
▪ Content based:
▪ retrieve images based on content of the query image
▪ Deep learning models to represent images:
▪ extract features from images using deep learning models
Content-Based Image Retrieval
Data: ImageNet
▪ Contains over 1 million images with 1000 categories
▪ Large and diversified enough for image retrieval
▪ Not too large to fit into the disk space of a desktop
▪ There are many pre-trained models trained on ImageNet
Content-Based Image Retrieval
Deep learning model as image representation


Our project: ResNet-50
Features: extracted from the last pooling layer of ResNet-50
Content-Based Image Retrieval
Architecture
▪ Web interface UI
▪ Front-end server
▪ Feature server
▪ Image server
▪ Indexer
▪ Dimension Reducer
Content-Based Image Retrieval
Content-Based Image Retrieval
Demo and Results
▪ http://98.7.92.164/home.html
Content-Based Image Retrieval
Thank you!
Reference
▪ Deep Learning for Content-Based Image Retrieval: A Comprehensive Study. J
Wan, D Wang, SCH Hoi, P Wu, J Zhu, Y Zhang, J Li
▪ Using very deep autoencoders for content-based image retrieval. 2011. A
Krizhevsky, and GE Hinton
▪ Neural codes for image retrieval. 2014. Artem BabenkoAnton SlesarevAlexandr
▪ Deep image retrieval: Learning global representations for image search. 2016. A
Gordo, J Almazán, J Revaud, D Larlus
▪ End-to-end learning of deep visual representations for image retrieval. 2016. S
Jain, T Zaveri, K Prajapati, S Patel
▪ Sennrich et al. Neural Machine Translation of Rare Words with Subword units.
ACL’16
Reference
▪ Compression of Deep Neural Networks for Image Instance Retrieval. 2017. Vijay
Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Lingyu
Duan, Tomaso Poggio