- Content Based Image Clustering and Image Retrieval(CBIR)
Content-based Image Retrieval (CBIR) is the application of computer vision to the problem of searching for digital images in large databases. The main objective of this project is to develop an object-based image retrieval system, which incorporates Relevance Feedback (RF) and semantic clustering techniques to improve the accuracy and efficiency of the retrieval.
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- Image Spam Mining
Image spam prevents text based spam filters from detecting and blocking spam messages. This project investigates image spam with image segmentation and image clustering techniques in order to reveal the common sources, i.e. the spammers, of unsolicited emails.
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- Spatio-temporal Event Mining for Surveillance Video Databases
The major objective of this project is to develop an event mining system for surveillance video databases that incorporates various techniques from computer vision and spatio-temporal data mining such as object tracking, classification, and abnormal event (e.g., accidents and illegal U-turns) detection.
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- imArray
imArray is a NSF funded project carried out in collaboration with the Department of Biostatistics at UAB. We propose to develop an automatic high-performance microarray scanner software, which is intended to provide a comprehensive data and information management environment for microarray image analysis and microarray data mining from multi-modalities.
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- SEQOPTICS: Protein Sequence Clustering with Optics
SEQOPTICS is a data mining tool that clusters protein sequences on the basis of OPTICS (Ordering Points To Identify the Clustering Structure). SEQOPTICS emphasizes the visualization of results and supports interactive work.
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- Outlier Detection
An outlier is an observation that is numerically distant from the rest of the data. We introduce a novel method to find the outliers and strong outlier groups, based on the Maximum Flow Minimum Cut theorem from graph theory, and evaluate the outliers by outlier degrees.
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