1. MULTIMEDIA SYSTEMS CBIR & CBVR

2. Schedule Image Annotation (CBIR) Image Annotation (CBIR) Video Annotation (CBVR) Video Annotation (CBVR) Few Project Ideas Few Project Ideas

3. CBIR Content-based image retrieval (CBIR) Content-based image retrieval (CBIR) (Color, Shape and Texture) (Color, Shape and Texture)Demo

4. CBVR Content Based Video Retrieval – 3 steps Feature Extraction (Color, Shape & Texture) Feature Extraction (Color, Shape & Texture) Temporal Analysis Temporal Analysis Motion Feature space (Usually data is available in video format e.g. MPEG, Make use of this information. Or you can figure out you own information) Indexing (Semantic web) Indexing (Semantic web) References References http://viper.unige.ch/~marchand/CBVR/ http://viper.unige.ch/~marchand/CBVR/http://viper.unige.ch/~marchand/CBVR/

5. Demo Face Recognition Face Recognition Image Retrieval Image Retrieval

6. Software & Tools Feature Extraction Oracle 11g Oracle 11g OpenCV, Java Advanced Imaging API OpenCV, Java Advanced Imaging API Matlab Toolkits Matlab ToolkitsStorage Google File System (Hadoop) Google File System (Hadoop) Map Reduce Sematic Web Jena, Protégé Jena, ProtégéCrawlers Arale, Arachnid Arale, Arachnid AraleArachnid AraleArachnid

7. Projects Face recognition (Demo) Face recognition (Demo) Given a set of training face images find the most probable face classification. You have matlab toolkit, OpenCV to help you. Techniques to use are SVM, HMM, Neural Networks. Image Annotation using various techniques Image Annotation using various techniques Classify a set of features (For example ) Video Annotation Video Annotation Semantic Annotation Semantic Annotation Keyword refinement Keyword refinement

8. Note of Caution Be aware of the semantic gap. Be aware of the semantic gap.

9. Sancho Sebastine Sancho Sebastine Email: sancho.nitw@gmail.com