This project address the problem of registering a sequence of images in a moving dynamic texture video.

Project-Image Retrieval

Recent image retrieval algorithms based on local features indexed by a vocabulary tree and holistic features indexed by compact hashing codes both demonstrate excellent scalability. However, their retrieval precision may vary dramatically among queries. This motivates us to investigate how to fuse the ordered retrieval sets given by multiple retrieval methods, to further enhance the retrieval precision.


Automatically assigning relevant text keywords to images is an important problem. We introduce a regularization based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task.


This projects address the issue of motion saliency detection in video sequences.


We propose a framework for obtaining transformation-invariant image sparse representation. W can simultaneously recover the sparse representation of a target image and the image plane transformation between the target and the model images.