@mastersthesis {xEscue, title = {Bundling interest points for object classification}, year = {2014}, abstract = {

Advisors: Xavier Giro-i-Nieto and Carles Ventura-Royo

Bundling interest points for object classification from Xavi Gir{\'o}

This Bachelor of Science thesis addresses the problem of image classification combining two popular visual representations: points and regions. Firstly, the study explores bundling interest points with regions. These regions are generated with an initial SLIC partition and using Binary Partition Tree (BPT), considering different scales of resolution in the segmentation. Secondly, it explores modelling visual classes as a group of points extracted from different images. Based on Naive-Bayes Nearest Neighbor (NBNN), we are using 1-Nearest Neighbor with SURF descriptor on the 17 Category Flower Dataset with 1360 images of flowers distributed into 17 classes, 80 images per class. We have verified that grouping interest points of the same class improves the F1-score a 9.2\%. However, bundling interest points into regions using segmentation worsens the F1-score between 1\% and 7\%, depending on the number of regions in the segmentation.

[Extended summary on Bitsearch blog]

Author{\textquoteright}s website: jordisanchez.info

Grade: A (9.3/10)

(This BSc thesis was written in Catalan language.)

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Aquest Treball Final de Grau aborda el problema de la classificaci{\'o} d{\textquoteright}imatges combinant dues representacions visuals populars: els punts i les regions. En primer lloc, l{\textquoteright}estudi explora l{\textquoteright}agrupaci{\'o} de punts d{\textquoteright}inter{\`e}s amb les regions. Aquestes regions es generen amb una partici{\'o} inicial SLIC i s{\textquoteright}utilitzen els Arbres de Partici{\'o} Bin{\`a}ria (Binary Partition Tree, BPT), considerant diferents escales de resoluci{\'o} en la segmentaci{\'o}. En segon lloc, s{\textquoteright}estudia modelar les classes com a grup de punts d{\textquoteright}inter{\`e}s extrets d{\textquoteright}imatges diferents. Basant-nos en el classificador Naive-Bayes Nearest Neighbor (NBNN), hem utilitzat el ve{\"\i} m{\'e}s proper amb un descriptor SURF sobre la base de dades 17 Category Flower Dataset, que cont{\'e} 1360 imatges de flors distribu{\"\i}des en 17 classes, amb 80 imatges per classe. Hem pogut verificar que el fet d{\textquoteright}ajuntar els punts d{\textquoteright}inter{\`e}s de les imatges d{\textquoteright}una mateixa classe millora la puntuaci{\'o} F1 en un 9,2\%. No obstant, l{\textquoteright}agrupaci{\'o} de punts d{\textquoteright}inter{\`e}s en regions utilitzant una segmentaci{\'o} de la imatge empitjora la puntuaci{\'o} F1 entre l{\textquoteright}1\% i el 7\%, depenent del nombre de regions de la segmentaci{\'o}.

Lloc web de l{\textquoteright}autor: jordisanchez.info

Qualificaci{\'o}: A (9.3/10)

Jordi Sanchez Escue

}, keywords = {Bundling interest points, Digital Images, image classification, Images, Nearest Neighbor, SURF}, url = {http://hdl.handle.net/2099.1/22672}, author = {S{\'a}nchez-Escu{\'e}, Jordi}, editor = {Ventura, C. and Xavier Gir{\'o}-i-Nieto} }