PPT-Similarity Search in Visual Data

PhD Thesis Defense Anoop Cherian Department of Computer Science and Engineering University of Minnesota TwinCities Adviser Prof Nikolaos Papanikolopoulos Contact cheriancsumnedu. Presented by:. Akshay. Kumar. Pankaj. . Prateek. Are these similar?. Number ‘1’ vs. color ‘red’. Number ‘1’ vs. ‘small’. Horse vs. Rider. True vs. false . ‘. Monalisa. ’ vs. ‘Virgin of the rocks’. Sarah Theobald & . Nestor Matthews. Department of Psychology, Denison University, Granville OH 43023 USA. . . The human brain is constantly being presented with complex visual information from all locations. As the retina receives information from either the left or right visual field, or hemifield, the information is processed predominately in the contralateral hemisphere. The brain’s ability to integrate visual information in the cortex allows for a perceptually unified experience when receiving visual information from all locations. However, not all lateralities are “created equal”. . An Adaptive Framework for Similarity Join and Search. Jiannan. Wang. . (Tsinghua University). Guoliang. . Li (Tsinghua . University). Jianhua. . Feng. (Tsinghua University). Data Integration. Data Cleaning. Speaker: . Rui-Rui. Li. Supervisor: Prof. Ben Kao. 1. Outline. Introduction. Motivation. Methods. -using click-through data. -using session data. -context aware query suggestion. -other methods. Conclusion. Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining . their . similarities (or dissimilarities). Thomas Berg and Peter N. . Belhumeur. Columbia University. Outline. Introduction. Visual . Similarity. A Visual Field Guide to . Birds. Conclusions. Introduction. “Can a . recognition system show humans what to look for . Stanford University. Autumn 2009. Instructors. : . Andrei . Broder . and Vanja Josifovski. Introduction to Computational Advertising. 1. General course info. Course Website. : . http://www.stanford.edu/class/msande239/. Summarizing Web Pages for Search and Revisitation. Jaime Teevan, Ed Cutrell, Danyel Fisher, Steven Drucker, . G. onzalo Ramos, Paul André. 1. , Chang Hu. 2. Microsoft Corporation. 1. University of Southampton . Jing . Zhang, . Jie. . Tang . , Cong . Ma . , . Hanghang. . Tong . , Yu . Jing . , and . Juanzi. . Li. Presented by Moumita Chanda Das . Outline. Introduction. Problem formulation. Panther using path sampling. a Multi-Layered Indexing Approach. Yongjiang Liang, . Peixiang Zhao. CS @ FSU. zhao@cs.fsu.edu. Outline. Introduction. State-of-the-art solutions. ML-Index & similarity search. Experiments. Conclusion. Raymond . Veon. Assistant Dean for Arts Education,. Caine College of the Arts. Utah State University. Visual Analogy .  . Analogy. : 1. [n] . similarity. in some respect . between things. that are . Summarizing Web Pages for Search and Revisitation. Jaime Teevan, Ed Cutrell, Danyel Fisher, Steven Drucker, . G. onzalo Ramos, Paul André. 1. , Chang Hu. 2. Microsoft Corporation. 1. University of Southampton . Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining in terms of their similarities (or dissimilarities). Scott Wen-tau Yih. Joint work with. Kristina Toutanova, John Platt, Chris Meek. Microsoft Research. Cross-language Document Retrieval. English Query Doc. Spanish Document Set. Web Search & Advertising.