Incremental learning for querying multimodal symbolic data.
In this thesis we present an incremental learning algorithm for learning and classifying the pattern of movement of multiple objects in a dynamic scene. The method that we describe is based on symbolic representations of the patterns. The typical representation has a spatial component that describes...
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| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2000
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| Online Access: | http://hdl.handle.net/20.500.11937/1660 |
| _version_ | 1848743731462668288 |
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| author | Lazarescu, Mihai M. |
| author_facet | Lazarescu, Mihai M. |
| author_sort | Lazarescu, Mihai M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this thesis we present an incremental learning algorithm for learning and classifying the pattern of movement of multiple objects in a dynamic scene. The method that we describe is based on symbolic representations of the patterns. The typical representation has a spatial component that describes the relationships of the objects and a temporal component that describes the ordering of the actions of the objects in the scene. The incremental learning algorithm (ILF) uses evidence based forgetting, generates compact concept structures and can track concept drift.We also present two novel algorithms that combine incremental learning and image analysis. The first algorithm is used in an American Football application and shows how natural language parsing can be combined with image processing and expert background knowledge to address the difficult problem of classifying and learning American Football plays. We present in detail the model developed to representAmerican Football plays, the parser used to process the transcript of the American Football commentary and the algorithms developed to label the players and classify the queries. The second algorithm is used in a cricket application. It combines incremental machine learning and camera motion estimation to classify and learn common cricket shots. We describe the method used to extract and convert the camera motion parameter values to symbolic form and the processing involved in learning the shots.Finally, we explore the issues that arise from combining incremental learning with incremental recognition. Two methods that combine incremental recognition and incremental learning are presented along with a comparison between the algorithms. |
| first_indexed | 2025-11-14T05:50:14Z |
| format | Thesis |
| id | curtin-20.500.11937-1660 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:50:14Z |
| publishDate | 2000 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-16602017-02-20T06:38:47Z Incremental learning for querying multimodal symbolic data. Lazarescu, Mihai M. multiple object movement pattern representation incremental learning ILF multimodal symbolic data In this thesis we present an incremental learning algorithm for learning and classifying the pattern of movement of multiple objects in a dynamic scene. The method that we describe is based on symbolic representations of the patterns. The typical representation has a spatial component that describes the relationships of the objects and a temporal component that describes the ordering of the actions of the objects in the scene. The incremental learning algorithm (ILF) uses evidence based forgetting, generates compact concept structures and can track concept drift.We also present two novel algorithms that combine incremental learning and image analysis. The first algorithm is used in an American Football application and shows how natural language parsing can be combined with image processing and expert background knowledge to address the difficult problem of classifying and learning American Football plays. We present in detail the model developed to representAmerican Football plays, the parser used to process the transcript of the American Football commentary and the algorithms developed to label the players and classify the queries. The second algorithm is used in a cricket application. It combines incremental machine learning and camera motion estimation to classify and learn common cricket shots. We describe the method used to extract and convert the camera motion parameter values to symbolic form and the processing involved in learning the shots.Finally, we explore the issues that arise from combining incremental learning with incremental recognition. Two methods that combine incremental recognition and incremental learning are presented along with a comparison between the algorithms. 2000 Thesis http://hdl.handle.net/20.500.11937/1660 en Curtin University fulltext |
| spellingShingle | multiple object movement pattern representation incremental learning ILF multimodal symbolic data Lazarescu, Mihai M. Incremental learning for querying multimodal symbolic data. |
| title | Incremental learning for querying multimodal symbolic data. |
| title_full | Incremental learning for querying multimodal symbolic data. |
| title_fullStr | Incremental learning for querying multimodal symbolic data. |
| title_full_unstemmed | Incremental learning for querying multimodal symbolic data. |
| title_short | Incremental learning for querying multimodal symbolic data. |
| title_sort | incremental learning for querying multimodal symbolic data. |
| topic | multiple object movement pattern representation incremental learning ILF multimodal symbolic data |
| url | http://hdl.handle.net/20.500.11937/1660 |