Here are my projects. Some of them are my academy research, and some are just my interest.

cnn-cbir-benchmark

CNN CBIR Benchmark

My role: Main developer

Project: cnn-cbir-benchmark

CNN CBIR Benchmark tries to build a benchmark for image retrieval, particularly for object retrieval. The project has finished three CNN-based CBIR methods and one hand-crafted feature (i.e. fisher vector) method. The project has just released one FC image retrieval method. Everything is still going on.

Technologies: CNN features, Hand-crafted features

SeetaFaceLib

My role: Main developer

Code: SeetaFaceLib

SeetaFaceLib is face image retrieval and recognition project based on SeetaFaceEngine. I work in a Mac OS envirment, so I'd like to make the SeetaFaceEngine work in Mac OS. I have make some changes to make it work in Xcode. More importantly, I have also built a QT project of face image retrieval.

Technologies: Image Retrieval, Face Recognition, OpenCV, C++, QT

PupilTracker

My role: Main developer

The information of pupilTracker will be coming soon.

Technologies: Image Proccessing, OpenCV, C++

Oxford Screenshot

DuplicateSearch

Search results: Duplicate Search

My role: Develop independently

Convolutional Neural Network has been sucessfully applied to image classification, object detection, etc. However, It's not easy to apply it to duplicate search or object retrieval due to the difficult of data enhancement. Duplicate search can be used to many business applications such as searching the same clothes or shoes on Taobao, and I believe it has great application vulae. So after diving into image retrieval based on key point feature such as SIFT, I implement a prototype system based on bag of feature. The performance of the prototype image retrieval system works very well, and the mAP can be achieve to 83.35%. Moreover. I test it on 1500,000 clothes images, and the search result can be found here.

Technologies: SIFT, BoVW, Image Retrieval

Blog post: BoF, VLAD and FV for image retrieval (Chinese)

PicSearch Screenshot

PicSearch

Online demo: CNN for CBIR (offline now)

Video: natural images, medical images

Code: feature extraction, search online

My role: Develop independently

After researching CBIR one year, I determined to develop a image retrieval engine demo just for interest. The convolutional neural networks in the comuter vision community develop so fast in recent years and show great performance with various tasks. So in my spare time I dive to read papers of CNN and program. The dataset of PicSearch engine is Caltech256, which contains 29780 images, and until now it supports query in the dataset only.

Technologies: CNN, Python, Image Retrieval

Blog post: Image retrieval using MatconvNet and pre-trained imageNet (Chinese)

HABIR Screenshot

HABIR

Project: Hashing Baseline for Image Retrieval

My role: Develop independently with open sources

I do the image retrieval research in early 2013. My mission is to design efficient hashing algorithm to map the semantic similar images to the similar codes. There are two main advantages using hashing method for image retrieval, i.e. storage and computation efficience. To let more researchers focus on design hashing algorithm, I have built a hashing baseline, hoping this project can do some help for some researchers.

Technologies: Matlab, Hashing, Image Retrieval

PCVwithPython Screenshot

PCVwithPython

Project: Programming Computer Vision with Python(Translate to Chinese)

My role: equal contribution with one partner

I'm a big fun of Python. I think Python is one of the most elegant programming language I have known. It's a great power tool which can be used everywhere, such as write scripts, using Django for web developmentand do science computation. When I first meet the book , I was attracted by the book. The content in this book is quite rich and diverse and I can't bear to share the book to more people. So in my free time, I dived into the book and translate it into Chinese. Morover, I organized and supply the codes.

Technologies: Python, Jekyll, Markdown

Interested? Here's my Github and resume, let's get in touch.