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- W2201828123 abstract "Model-based approaches have become important tools to model data and infer knowledge. Such approaches are often used for clustering and object recognition which are crucial steps in many applications, including but not limited to, recommendation systems, search engines, cyber security, surveillance and object tracking. Many of these applications have the urgent need to reduce the semantic gap of data representation between the system level and the human being understandable level. Indeed, the low level features extracted to represent a given object can be confusing to machines which cannot differentiate between very similar objects trivially distinguishable by human beings (e.g. apple vs tomato). Such a semantic gap between the system and the user perception for data, makes the modeling process hard to be designed basing on the features space only. Moreover those models should be flexible and updatable when new data are introduced to the system. Thus, apart from estimating the model parameters, the system should be somehow informed how new data should be perceived according to some criteria in order to establish model updates. In this thesis we propose a methodology for data representation using a hierarchical mixture model basing on the inverted Dirichlet and the generalized inverted Dirichlet distributions. The proposed approach allows to model a given object class by a set of components deduced by the system and grouped according to labeled training data representing the human level semantic. We propose an update strategy to the system components that takes into account adjustable metrics representing users perception. We also consider the page zero problem in image retrieval systems when a given user does not possess adequate tools and semantics to express what he/she is looking for, while he/she can visually identify it. We propose a statistical framework that enables users to start a search process and interact with the system in order to find their target mental image. Finally we propose to improve our models by using a variational Bayesian inference to learn generalized inverted Dirichlet mixtures with features selection. The merit of our approaches is evaluated using extensive simulations and real life applications." @default.
- W2201828123 created "2016-06-24" @default.
- W2201828123 creator A5001475115 @default.
- W2201828123 date "2015-06-22" @default.
- W2201828123 modified "2023-09-26" @default.
- W2201828123 title "Mixture Models for Multidimensional Positive Data Clustering with Applications to Image Categorization and Retrieval" @default.
- W2201828123 cites W1501567504 @default.
- W2201828123 cites W1506806321 @default.
- W2201828123 cites W1516111018 @default.
- W2201828123 cites W1519488069 @default.
- W2201828123 cites W1534506107 @default.
- W2201828123 cites W1566135517 @default.
- W2201828123 cites W1579271636 @default.
- W2201828123 cites W1680392829 @default.
- W2201828123 cites W1964721292 @default.
- W2201828123 cites W1965683262 @default.
- W2201828123 cites W1966502880 @default.
- W2201828123 cites W1968913939 @default.
- W2201828123 cites W1972305392 @default.
- W2201828123 cites W1973693867 @default.
- W2201828123 cites W1980014655 @default.
- W2201828123 cites W1984526963 @default.
- W2201828123 cites W1992419399 @default.
- W2201828123 cites W1993577356 @default.
- W2201828123 cites W1996623543 @default.
- W2201828123 cites W2000241167 @default.
- W2201828123 cites W2000545950 @default.
- W2201828123 cites W2005114773 @default.
- W2201828123 cites W2007413993 @default.
- W2201828123 cites W2008225289 @default.
- W2201828123 cites W2010486392 @default.
- W2201828123 cites W2011832962 @default.
- W2201828123 cites W2012354925 @default.
- W2201828123 cites W2020018100 @default.
- W2201828123 cites W2021783597 @default.
- W2201828123 cites W2026302857 @default.
- W2201828123 cites W2033906329 @default.
- W2201828123 cites W2035893370 @default.
- W2201828123 cites W2036536917 @default.
- W2201828123 cites W2038458814 @default.
- W2201828123 cites W2041993031 @default.
- W2201828123 cites W2044812163 @default.
- W2201828123 cites W2045494549 @default.
- W2201828123 cites W2045664231 @default.
- W2201828123 cites W2049633694 @default.
- W2201828123 cites W2054658115 @default.
- W2201828123 cites W2060003092 @default.
- W2201828123 cites W2060556149 @default.
- W2201828123 cites W2061838123 @default.
- W2201828123 cites W2067697112 @default.
- W2201828123 cites W2071274604 @default.
- W2201828123 cites W2072169887 @default.
- W2201828123 cites W2074066709 @default.
- W2201828123 cites W2075330621 @default.
- W2201828123 cites W207560109 @default.
- W2201828123 cites W2080972498 @default.
- W2201828123 cites W2082831393 @default.
- W2201828123 cites W2086704137 @default.
- W2201828123 cites W2087544865 @default.
- W2201828123 cites W2093150403 @default.
- W2201828123 cites W2095610590 @default.
- W2201828123 cites W2096091969 @default.
- W2201828123 cites W2096784803 @default.
- W2201828123 cites W2101498401 @default.
- W2201828123 cites W2102862543 @default.
- W2201828123 cites W2103976974 @default.
- W2201828123 cites W2104120904 @default.
- W2201828123 cites W2110437031 @default.
- W2201828123 cites W2111389053 @default.
- W2201828123 cites W2111918405 @default.
- W2201828123 cites W2112796928 @default.
- W2201828123 cites W2113021635 @default.
- W2201828123 cites W2113447708 @default.
- W2201828123 cites W2113651538 @default.
- W2201828123 cites W2120000263 @default.
- W2201828123 cites W2120211304 @default.
- W2201828123 cites W2120486209 @default.
- W2201828123 cites W2122305792 @default.
- W2201828123 cites W2122882636 @default.
- W2201828123 cites W2123602281 @default.
- W2201828123 cites W2127498532 @default.
- W2201828123 cites W2128888182 @default.
- W2201828123 cites W2130046005 @default.
- W2201828123 cites W2130293653 @default.
- W2201828123 cites W2130453506 @default.
- W2201828123 cites W2130660124 @default.
- W2201828123 cites W2131717199 @default.
- W2201828123 cites W2133703553 @default.
- W2201828123 cites W2134000477 @default.
- W2201828123 cites W2135194391 @default.
- W2201828123 cites W2137483768 @default.
- W2201828123 cites W2137587644 @default.
- W2201828123 cites W2139818818 @default.
- W2201828123 cites W2140834120 @default.
- W2201828123 cites W2143343451 @default.
- W2201828123 cites W2144679084 @default.
- W2201828123 cites W2152594362 @default.
- W2201828123 cites W2153138305 @default.
- W2201828123 cites W2153233077 @default.
- W2201828123 cites W2155099190 @default.