{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/77019"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/77019","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Detecting high level story elements from low level data","abstract":"The problem addressed here is yet another artificial intelligence problem that is easily solved by young children yet challenges even sophisticated computer programs. This thesis's canonical example is a scene featuring two entities drinking. In one scene, a cat drinks from a faucet. In the other, a human drinks from a glass. Even young humans can identify that the two images are similar in that they both involve drinking. However, low-level analysis of the scene will find many more differences than similarities in the case cited above. In my research examines ways to detect high-level story elements such as drinking from low-level data such as that which might be produced from analyzing pictures and videos directly. I present a system that accepts as input a collection of high-level events represented in transition space. I analyze, then select the affinity propagation clustering algorithm to group the events using only their low-level representations. To this end, I present a novel algorithm for determining how similar any two points in transition space are. Due to the lack of vision systems capable of providing a varied dataset, I create a system which translates English language descriptions of high-level events and produces a specially formatted transition space file. To support my hypotheses, I presents the results of two experiments using the system described in this thesis. The first experiment uses English language files and the second uses data produced from a set of experimental videos. Using the English language files the system was able to detect groups corresponding to flying and walking among others out of a total set of 16 events.","abstract_html":"The problem addressed here is yet another artificial intelligence problem that is easily solved by young children yet challenges even sophisticated computer programs. This thesis's canonical example is a scene featuring two entities drinking. In one scene, a cat drinks from a faucet. In the other, a human drinks from a glass. Even young humans can identify that the two images are similar in that they both involve drinking. However, low-level analysis of the scene will find many more differences than similarities in the case cited above. In my research examines ways to detect high-level story elements such as drinking from low-level data such as that which might be produced from analyzing pictures and videos directly. I present a system that accepts as input a collection of high-level events represented in transition space. I analyze, then select the affinity propagation clustering algorithm to group the events using only their low-level representations. To this end, I present a novel algorithm for determining how similar any two points in transition space are. Due to the lack of vision systems capable of providing a varied dataset, I create a system which translates English language descriptions of high-level events and produces a specially formatted transition space file. To support my hypotheses, I presents the results of two experiments using the system described in this thesis. The first experiment uses English language files and the second uses data produced from a set of experimental videos. Using the English language files the system was able to detect groups corresponding to flying and walking among others out of a total set of 16 events.","abstract_has_math":false,"creators":["Speed, Erek R"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Patrick H. Winston."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:21:18Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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I present a system that accepts as input a collection of high-level events represented in transition space. I analyze, then select the affinity propagation clustering algorithm to group the events using only their low-level representations. To this end, I present a novel algorithm for determining how similar any two points in transition space are. Due to the lack of vision systems capable of providing a varied dataset, I create a system which translates English language descriptions of high-level events and produces a specially formatted transition space file. To support my hypotheses, I presents the results of two experiments using the system described in this thesis. The first experiment uses English language files and the second uses data produced from a set of experimental videos. Using the English language files the system was able to detect groups corresponding to flying and walking among others out of a total set of 16 events."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Detecting high level story elements from low level data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Patrick H. Winston."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Speed, Erek R"],"dc:date.accessioned":["2013-02-14T15:38:57Z"],"dc:date.available":["2013-02-14T15:38:57Z"],"dc:date.issued":["2012"],"dc:description":["Thesis (M. 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I present a system that accepts as input a collection of high-level events represented in transition space. I analyze, then select the affinity propagation clustering algorithm to group the events using only their low-level representations. To this end, I present a novel algorithm for determining how similar any two points in transition space are. Due to the lack of vision systems capable of providing a varied dataset, I create a system which translates English language descriptions of high-level events and produces a specially formatted transition space file. To support my hypotheses, I presents the results of two experiments using the system described in this thesis. The first experiment uses English language files and the second uses data produced from a set of experimental videos. 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