[ca] Laparici de bases de dades amb un gran nombre dimatges requereix del desen- volupament de representacions dimatge eficients i algorismes que siguin capaos de manipular dades a gran escala de manera eficient. Avui dia, la representaci dimatges basada en la tcnica de Bagsof- Words (Bow), s lenfocament ms ha reeixit en el context de les tasques de classificaci dobjectes i descenes. No obstant aix, el seu principal desavantatge s labsncia dinformaci espacial. En aquest context, ls de pirmides espacials (SP) sha aplicat amb xit per incorporar la informaci espacial: bsicament es tracta de subdividir la imatge recursivament subregions al duplicar el nombre de divisions en cada direcci de la imatge, per- metent lextracci dhistogrames i altres caracterstiques en aquestes subregions. A partir del notable rendiment que sobt amb les pirmides espacials, el seu s sha generalitzat en un creixent nombre daplicacions i problemes de visi, pel que en aquesta Tesi hem analitzat com millorar-ne el seu s. Dins el marc de les SP, hem estudiat la forma ptima per obtenir una representaci de la imatge que sigui capa de fer front als defectes ms destacats de les SP, en concret, la seva alta dimensionalitat i la seva rigidesa de la representaci de la imatge resultant . En resum, la preocupaci principal daquesta Tesi s la recerca dels lmits de les pirmides espacials al tractar de trobar solucions per als seus inconvenients. En general, aquesta Tesi explora el problema dobtenir representacions molt com- pactes i adaptades a la naturalesa de la imatge que siguin molt ms informatives en el context de la classificaci dobjectes i escenes. A la primera part daquesta Tesi, primer sanalitzen les implicacions de laplicaci directa de lestat de lart en les tcniques de compressi per obtenir BoWs compactes basada en una representaci de la imatge basada en pirmides espacials. Desprs introdum una nova tcnica de compressi de SP que funciona a dos nivells: (i) comprensi de les caracterstiques de la pirmide espacial, seguit per ii) compressi de les regions menys informa- tives de la SP amb el propsit dobtenir pirmides compactes i adaptatives . A continuaci, introdum un nou descriptor de textura que representa laparena local de la imatge. La textura es representa com un vector compacte adequat per al seu s en algorismes daprenentatge. Els resultats experimentals mostren que la informaci de la imatge permet superar els mtodes que utilitzen noms la forma o laparena dels pxels. La representaci resultant s una pirmide espacial que obt un gran rendiment al aplicar-lo en tasques de classificaci descenes. A la segona part daquesta tesi, es presenta una nova tcnica per a la construcci adaptativa de pirmides. En particular, sinvestiguen diversos enfocaments per a laprenentatge duna organitzaci espacial de la imatge que estigui especialment adaptada per a la tasca de classificaci. Amb aquesta finalitat, sanalitza ls de (i) geometries genriques descenes 3D, on la geometria duna escena sobt sobre la base estadstica duna base de dades dimatges, i de ls de (ii) particions espacials discriminatives generades utilitzant mesures utilitzades en Teoria de la Informaci. El mtode proposat sha provat en diversos conjunts de dades de classificaci dobjectes i els resultats demostrant clarament leficcia de la utilitzaci de les representacions espacials adaptadasa la geometria 3D present a la imatge. En la tercera part daquesta tesi, sinvestiga el problema dobtenir una representaci compacta de

[en] Abstract The release of challenging datasets with a vast number of images, re- quires the development of efficient image representations and algorithms which are able to manipulate these largescale datasets efficiently. Nowadays the Bag- of-Words (BoW) based image representation is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based im- age representation. The main SP approach, works by repeatedly sub-dividing the image into increasingly finer sub-regions by doubling the number of di- visions on each axis direction, and further computing histograms of features over the resulting sub-regions. Observing the remarkable performance of spa- tial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation which is able to cope with its most foremost shortcomings, concretely, its high dimensionality and the rigidity of the resulting image representation still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them. This thesis explores the problem of obtaining compact, adaptive, yet informative spatial image repre- sentations in the context of object and scene classification tasks. In the first part of this thesis, we first analyze the implications of directly applying the state-of-the-art compression techniques for obtaining compact BoW-based im- age representation within the context of spatial pyramids. We then introduce a novel SP compression technique that works on two levels; (i) compressing the least informative spatial pyramid features, followed by, (ii) compressing the least informative SP regions for the purpose of obtaining compact, and adaptable SP. We then introduce a new texture descriptor that represents local image texture and its spatial layout. Texture is represented as a compact vector descriptor suitable for use in standard learning algorithms with kernels. Experimental re- sults show that texture information has similar classification performances and sometimes outperforms those methods using only shape or appearance informa- tion. The resulting spatial pyramid representation demonstrates significantly improved performance on challenging scene classification tasks. In the second part of this thesis, we present a novel technique for building adaptive spatial pyramids. In particular, we investigate various approaches for learning adap- tive spatial pyramids, which are specially tailored for the task at hand. To this end, we analyze the use of (i) standard generic 3D scene geometries; the geometry of a scene is measured based on image statistics taken from a sin- gle image. (ii) discriminative spatial partitionings, which are generated based on an information-theoretic approach. The proposed method is tested on sev- eral challenging benchmark object classification datasets. The results clearly demonstrated the effectiveness of using adaptive spatial representations, which are steered by the 3D scene geometry present in images. In the third part of 1 this thesis, we investigate the problem of obtaining compact spatial pyramid im- age representations for object and scene classification tasks. We present a novel framework for obtaining compact spatial pyramid image representation up to an order of magnitude without any significant reduction in accuracy. Moreover, we also investigate the optimal combination of multiple features such as color and shape within the context of our novel compact pyramid representation. Finally, we investigate the importance of using the spatial knowledge within the context of color constancy as an application. To this end, we present a novel framework for estimating the image illuminant based on spatial 3D geometry for learning the most appropriate color constancy algorithm to use for every image region. The final image illuminant is obtained based on a weighted combination of each individual illuminant-estimate obtained per region. We test and compare our performance to that of previous state-of-art methods. We will show that the set of innovations introduced here lead to a significant increase on performance on challenging color constancy datasets.