- author = "Andre Tavares {da Silva}",
- title_en = "Content-based image retrieval based on relevance feedback and optimum-path forest classifier",
- title = "Recuperacao de imagens por conteudo baseada em realimentacao de relevancia e classificador por floresta de caminhos otimos",
- school = "DEPARTAMENTO DE COMPUTACAO E AUTOMACAO INDUSTRIAL, FACULDADE DE ENGENHARIA ELETRICA E DE COMPUTACAO, UNIVERSIDADE ESTADUAL DE CAMPINAS",
- year = "2011",
- address = "Campinas, SP, Brazil",
- month = "26 " # jul,
- keywords = "genetic algorithms, genetic programming, SVM, CBIR, MPEG7, Horse Guards Parade, Pattern recognition, Information retrieval, Image analysis, Engenharia de Computacao, Reconhecimento de padroes, Recuperacao da informacao, Analise de imagem, Pattern recognition, Information retrieval, Image analysis",
-
URL = "
https://hdl.handle.net/20.500.12733/1616039",
-
URL = "https://bdtd.ibict.br/vufind/Record/UNICAMP-30_755750f6a29697ba188c0c2adb340442",
-
URL = "
https://www.dca.fee.unicamp.br/~leopini/private/teses-pdf/Tese-Andre_Tavares_da_Silva.pdf",
- size = "174 pages",
-
abstract = "Considering the increasing amount of image collections
that result from popularisation of the digital cameras
and the Internet, efficient search methods are becoming
increasingly necessary. In this context, this doctoral
dissertation proposes new methods for content-based
image retrieval based on relevance feedback and on the
OPF (optimum-path forest) classifier, being also the
first time that the OPF classifier is used in small
training sets.
This doctoral dissertation names as greedy and planned the two distinct learning paradigms for relevance feedback taking into account the returned images. The first paradigm attempts to return the images most relevant to the user at each iteration, while the second returns the images considered the most informative or difficult to be classified. The dissertation presents relevance feedback algorithms based on the OPF classifier using both paradigms with single descriptor.
Two techniques for combining descriptors are also presented along with the relevance feedback methods based on OPF to improve the effectiveness of the learning process. The first one, MSPS (Multi-Scale Search Parameter), is used for the first time in content-based image retrieval and the second is a consolidated technique based on genetic programming.
A new approach of relevance feedback using the OPF classifier at two levels of interest is also shown. In this approach it is possible to select the pixels in images at a level of interest and to choose the most relevant images at each iteration at another level.
This dissertation shows that the use of the OPF classifier for content based image retrieval is very efficient and effective, requiring few learning iterations to produce the desired results to the users. Simulations show that the proposed methods outperform the reference methods based on multi-point query and support vector machine. Besides, the methods based on optimum-path forest have shown to be on the average 52 times faster than the SVM-based approaches.",
-
notes = "In Portuguese.
Faculty of Electrical and Computer Engineering, UNICAMP.
Supervisor: Leo Pini Magalhaes, Co-orientador: Alexandre Xavier Falcao",
