Apostol Natsev

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In this paper we propose a novel method for multimedia semantic indexing using model vectors. Model vectors provide a semantic signature for multimedia documents by capturing the detection of concepts broadly across a lexicon using a set of independent binary classifiers. While recent techniques have been developed for detecting simple generic concepts such(More)
Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for(More)
This paper investigates the problem of incremental joins of multiple ranked data sets when the join condition is a list of arbitrary user-defined predicates on the input tuples. This problem arises in many important applications dealing with ordered inputs and multiple ranked data sets, and requiring the top k solutions. We use multimedia applications as(More)
In this paper we unify two supposedly distinct tasks in multimedia retrieval. One task involves answering queries with a few examples. The other involves learning models for semantic concepts, also with a few examples. In our view these two tasks are identical with the only differentiation being the number of examples that are available for training. Once(More)
Annotated collections of images and videos are a necessary basis for the successful development of multimedia retrieval systems. The underlying models of such systems rely heavily on quality and availability of large training collections. The annotation of large collections, however, is a time-consuming and error prone task as it has to be performed by(More)
We study the problem of semantic concept-based query expansion and re-ranking for multimedia retrieval. In particular, we explore the utility of a fixed lexicon of visual semantic concepts for automatic multimedia retrieval and re-ranking purposes. In this paper, we propose several new approaches for query expansion, in which textual keywords, visual(More)
We propose Semantic Model Vectors, an intermediate level semantic representation, as a basis for modeling and detecting complex events in unconstrained real-world videos, such as those from YouTube. The Semantic Model Vectors are extracted using a set of discriminative semantic classifiers, each being an ensemble of SVM models trained from thousands of(More)
Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other services with user-generated content have made a staggering amount of information (and misinformation) available. Government officials seek to leverage these resources to improve services and communication with citizens. Yet, the sheer volume of social data streams generates substantial noise(More)