Reverse nearest neighbor queries are useful in identifying objects that are of significant influence or importance. Existing methods either rely on pre-computation of nearest neighbor distances, do not scale well with high dimensionality, or do not produce exact solutions. In this work we motivate and investigate the problem of reverse nearest neighbor… (More)
Technology Center of Excellence. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. Abstract We describe a multilingual named entity recognition system using language independent feature templates, designed for processing short, informal… (More)
We introduce regression databases (REDB) to formalize and automate probabilistic querying using sparse learning sets. The REDB data model involves observation data, learning set data, views definitions, and a regression model instance. The observation data is a collection of relational tuples over a set of attributes; the learning data set involves a subset… (More)
Known Item Search (KIS) is a specialized task of the general multimedia search problem. It describes the scenario where a user has previously seen a video and wants to find it again in a large collection using a text description. While there exists only one correct answer to a query (or topic), the goal is to return a ranked list of videos most likely to… (More)
This paper describes KB Video Retrieval's participation in the TREC Video Retrieval Evaluation for 2010. This year we submitted results for the Semantic Indexing, Known-item Search, Instance Search, and Event Detection in Internet Multimedia tasks. Our goal this year was to evaluate ranking strategies and expand our knowledge based approach to a variety of… (More)
This paper describes the KB Video Retrieval system for the TRECVID 2009 evaluation. Our research focus this year was on query expansion using external knowledge bases.
This paper describes the Knowledge Base multimedia retrieval system for the TRECVID 2008 evaluation. Our focus this year is on query analysis and the creation of a topic knowledge base using external knowledge base information.
Multimedia retrieval suffers from the lack of common feature representation between a text based query and the visual content of a video repository. One approach to bridging this representation gap is known as query-by-concept, where a query and video are mapped into a common semantic feature space. One of the challenges with using semantic concepts for… (More)
Multimedia retrieval is a search and ranking task defined over multiple modalities. These modalities include speech, image, and text, which provide different views of the multimedia object. Queries to a multimedia retrieval system often take the form of a text only query and return a ranked result set which combines these multiple views. The text only query… (More)
Professor: Stephen Checkoway I managed three course assistants and held weekly office hours. Professor: Jason Eisner I led weekly discussion sections to cement concepts and improve problem solving skills. I supervised three course assistants in grading the assignments.