Aprendizaje de conceptos visuales basado en múltiples clasificadores
Object recognition is usually based on learning from a large dataset of previously selected training images; however, not all objects have one associated dataset. Nowadays it is possible to find images on Internet of virtually any object, only by launching a query with the object's name in a se...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2012 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/763 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/763 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Reconocimiento de objetos/Object recognition info:eu-repo/classification/Funciones/Sift features info:eu-repo/classification/Características locales/Local features info:eu-repo/classification/Affine transforms/Affine transforms info:eu-repo/classification/Filtros Gabor/Gabor filters info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Sumario: | Object recognition is usually based on learning from a large dataset of previously selected training images; however, not all objects have one associated dataset. Nowadays it is possible to find images on Internet of virtually any object, only by launching a query with the object's name in a search engine. Nevertheless, this strategy introduces computational challenges: (i) the object's name can have more than one meaning; (ii) the object can have different appearances, and (iii) without prior knowledge of the object it is hard to identify which visual features to use in order to train a classifier. In this thesis a method to tackle the above problem is proposed. The method incorporates an ensemble of classifiers that builds several object models treating intraclass variability. Responses of every classifier are combined to determine the presence or absence of the object. The method begins with a small training set obtained via Web, and a series of image transformations is applied in order to identify invariant features. The weight of the local and global features is optimized, in order to recognize different category types. We also develop an algorithm based on sliding windows to identify the object's position within an image. We evaluated our method on images from the Web, the Caltech-7 dataset and real environments. We compared our method against related work, obtaining competitive classification performance in the recognition of general and specific objects. |
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