DeepSketchHashing

This is the repository for reproducing some key results for our paper

• Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
• by Li Liu, Fumin Shen, Yuming Shen, Xianglong Liu and Ling Shao

to be presented on CVPR 2017 spotlight section. This work focuses on fast sketch-based image retrieval (SBIR) using binary codes.

Prerequisites

To produce the binary codes described in the paper, one needs to install Caffe beforehand.

The mid-level Sketch-Token representation is required for training the model. The codes can be found here. Please refer to the following papers for more details.

• J. M. Saavedra, J. M. Barrios, and S. Orand. Sketch based image retrieval using learned keyshapes (lks). in BMVC 2015.
• Lim, Joseph J., C. Lawrence Zitnick, and Piotr Dollár. Sketch tokens: A learned mid-level representation for contour and object detection. in CVPR. 2013.

Models

We provide several pretrained models on two datasets with their respective deploy files. You may try to use any of these models to produce hash code for image-sketch matching.

Sketchy Dataset (Extended)

• 64 bits
• 128 bits
• (UPDATED 4 AUG 2017!)The extra image data mentioned in the paper can be found here (NEW). The previous uploaded image data are wrong (apologize for this).

TU Berlin Sketch Dataset

• 64 bits
• 128 bits
• (UPDATED 21 AUG 2017!)The images of TU for our experiments can be found here.