Full text
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| Author(s): |
Lucas Moutinho Bueno
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Computação |
| Defense date: | 2011-08-19 |
| Examining board members: |
Ricardo da Silva Torres;
Humberto Luiz Razente;
Cid Carvalho de Souza
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| Advisor: | Ricardo da Silva Torres |
| Abstract | |
Local image descriptors are widely used in various applications for recognition of objects or scenes. Many local descriptors have been proposed in the literature to characterize points of interest in images. Among them are: PCA-SIFT, SIFT, GLOH, SURF, DAISY. Points of interest in images are determined by the detectors. Examples of detectors are Harris-Affine, Hessian-Affine, Fast Hessian, MSER, DoG. The objective of this work is to investigate the use of local descriptors in the context of content-based near-duplicate image retrieval, using hundreds of thousands of images. Content-based image retrieval aims at finding images in the database using the content of another image as a query, typically using descriptors. Near-duplicate images are determined by the deformation of an original image from geometric or radiometric transformations or occlusions. Due to the large number of points of interest computed on each of the hundreds of thousands images from database, exhaustive search techniques are not feasible on a large scale. Thus, methods such as Multicurves, LSH and Min-Hash, are designed to improve the speed of near-duplicate image retrieval. This work contributes to the state of the art in two major aspects. First, an analysis of local descriptors is carried out to evaluate the scalability of them. Second, an innovative system using Bayesian search is proposed to significantly decrease the amount of points of interest used in near-duplicate image retrieval, without significant loss of accuracy (AU) | |
| FAPESP's process: | 09/12826-5 - Comparative study of local image descriptors applied to content based image retrieval. |
| Grantee: | Lucas Moutinho Bueno |
| Support Opportunities: | Scholarships in Brazil - Master |