A framework for evaluating image tag assignment, tag refinement and tag-based image retrieval
Switch branches/tags
Nothing to show
Clone or download

README.md

Jingwei

Jingwei is an open-source testbed for evaluating methods for image tag assignment, tag refinement and tag-based image retrieval. It is developed as part of our survey effort, aiming to provide a timely reflection of the state-of-the-art in the field.

Dependencies

Training and Test Data

Setup

  • Modify Paths in start.sh (for linux/mac) and start.bat for windows.

This file includes several environment variables that the methods depend on, to select proper input and output folders. From a shell, you can prepare the environment for using the framework with:

$ source start.sh 
  • Configuration and Dependencies.

Depending on the method to be run, several different dependencies must be met and some external packages must be downloaded. The script setup.sh will report ready to run methods, depending on the available system packages. For some methods, it will also try to download and compile the provided libraries.

$ bash setup.sh 

Use a specific method

  • Scripts in doit provide step-by-step usages of each method.
  • Tutorials in samples show how to leverage the framework for solving varied tasks.

Methods implemented

Method Media Learning Code Platform
SemanticField tag instance-based python linux, windows
TagCooccur tag instance based Python linux, windows
TagRanking tag + image instance based Python linux, windows
KNN tag + image instance based C + Python linux, windows
TagVote tag + image instance based C + Python linux, windows
TagCooccur+ tag + image instance based C + Python linux, windows
TagProp tag + image model based C + Matlab + Python linux
TagFeature tag + image model based C + Python linux, windows
RelExample tag + image model based C + Python linux, windows
RobustPCA tag + image transduction based C + Matlab + Python linux

Code architecture: A high-level view

Citation

If you publish work that uses Jingwei, please consider cite our survey paper: Xirong Li, Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G. M. Snoek, Alberto Del Bimbo: Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval, ACM Computing Surveys (CSUR), Volume 49, Issue 1, 14:1-14:39, June 2016

Contact