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Automatic parsing of sports videos with grammars
Motivated by the analogies between languages and sports videos, we introduce a novel
approach for video parsing with grammars. It utilizes compiler techniques for integrating both semantic
annotation and syntactic analysis to generate a semantic index of events and a table of content for a given
sports video. The video sequence is first segmented and annotated by event detection with domain
knowledge. A grammar-based parser is then used to identify the structure of the video content.
Meanwhile, facilities for error handling are introduced which are particularly useful when the results of
automatic parsing need to be adjusted. As a case study, we have developed a system for video parsing in
the particular domain of TV diving programs. Experimental results indicate the proposed approach is
effectiv
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
Content-based Video Retrieval
no abstract
Grounding language in events
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 137-142).Broadcast video and virtual environments are just two of the growing number of domains in which language is embedded in multiple modalities of rich non-linguistic information. Applications for such multimodal domains are often based on traditional natural language processing techniques that ignore the connection between words and the non-linguistic context in which they are used. This thesis describes a methodology for representing these connections in models which ground the meaning of words in representations of events. Incorporating these grounded language models with text-based techniques significantly improves the performance of three multimodal applications: natural language understanding in videogames, sports video search and automatic speech recognition. Two approaches to representing the structure of events are presented and used to model the meaning of words. In the domain of virtual game worlds, a hand-designed hierarchical behavior grammar is used to explicitly represent all the various actions that an agent can take in a virtual world. This grammar is used to interpret events by parsing sequences of observed actions in order to generate hierarchical event structures. In the noisier and more open -ended domain of broadcast sports video, hierarchical temporal patterns are automatically mined from large corpora of unlabeled video data. The structure of events in video is represented by vectors of these hierarchical patterns.(cont.) Grounded language models are encoded using Hierarchical Bayesian models to represent the probability of words given elements of these event structures. These grounded language models are used to incorporate non-linguistic information into text-based approaches to multimodal applications. In the virtual game domain, this non-linguistic information improves natural language understanding for a virtual agent by nearly 10% and cuts in half the negative effects of noise caused by automatic speech recognition. For broadcast video of baseball and American football, video search systems that incorporate grounded language models are shown to perform up to 33% better than text-based systems. Further, systems for recognizing speech in baseball video that use grounded language models show 25% greater word accuracy than traditional systems.by Michael Ben Fleischman.Ph.D
The Meaning of Action:a review on action recognition and mapping
In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning
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