{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/77013"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/77013","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Using genetic algorithm to \"fool\" HMAX object recognition model","abstract":"HMAX (\"Hierarchical Model and X\") system is among the best machine vision approaches developed today, in many object recognition tasks [1]. HMAX decomposes an image into features which are passed to a classifier. These features each capture information about a small section of the input image but might not have information about the overall structure of the image if there is not a significant number of overlapping features. Therefore it can produce a false-positive if two images from two different classes having sufficiently similar features profile but completely different structures. To demonstrate the problem this thesis aimed to show that the features of a given subject are not unique because they lack geometric information. Genetic algorithm (GA) was used to create an image with a similar feature profile as a subject but which clearly does not belong to the subject. Using GA, random pixel images converged to an image whose feature profile has a small Euclidian distance from a target profile. This generated GA image does not resemble the target image but has a similar profile which successfully fooled the classifier in most cases. This implies that the \"binding problem\" is a major issue in a HMAX model of the size tested. Furthermore, methods of improving the system were investigated.","abstract_html":"HMAX ("Hierarchical Model and X") system is among the best machine vision approaches developed today, in many object recognition tasks [1]. HMAX decomposes an image into features which are passed to a classifier. These features each capture information about a small section of the input image but might not have information about the overall structure of the image if there is not a significant number of overlapping features. Therefore it can produce a false-positive if two images from two different classes having sufficiently similar features profile but completely different structures. To demonstrate the problem this thesis aimed to show that the features of a given subject are not unique because they lack geometric information. Genetic algorithm (GA) was used to create an image with a similar feature profile as a subject but which clearly does not belong to the subject. Using GA, random pixel images converged to an image whose feature profile has a small Euclidian distance from a target profile. This generated GA image does not resemble the target image but has a similar profile which successfully fooled the classifier in most cases. This implies that the "binding problem" is a major issue in a HMAX model of the size tested. Furthermore, methods of improving the system were investigated.","abstract_has_math":false,"creators":["Hasan, Maysun Mazhar"],"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":["Tomaso Poggio and Victor Chan."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:21:53Z","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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Therefore it can produce a false-positive if two images from two different classes having sufficiently similar features profile but completely different structures. To demonstrate the problem this thesis aimed to show that the features of a given subject are not unique because they lack geometric information. Genetic algorithm (GA) was used to create an image with a similar feature profile as a subject but which clearly does not belong to the subject. Using GA, random pixel images converged to an image whose feature profile has a small Euclidian distance from a target profile. This generated GA image does not resemble the target image but has a similar profile which successfully fooled the classifier in most cases. This implies that the \"binding problem\" is a major issue in a HMAX model of the size tested. 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