La représentation d'image est au cœur de beaucoup d'algorithmes de vision par ordinateur. Elle intervient notamment dans des tâches de reconnaissance de catégories visuelles comme la classification ou la détection d'objets. Dans ce contexte, la représentation "sac de mot visuel" (Bag of Visual Words ou BoVW en anglais) est l'une des méthodes de référence. Dans cette thèse, nous nous appuyons sur ce modèle pour proposer des représentations d'images discriminantes. Dans la première partie, nous présentons une nouvelle approche simple et efficace pour prendre en compte des informations spatiales dans le modèle BoVW. Son principe est de considérer l'orientation et la longueur de segments formés par des paires de descripteurs similaires. Une notion de "softsimilarité" est introduite pour définir ces relations intra et inter mots visuels. Nous montrons expérimentalement que notre méthode ajoute une information discriminante importante au modèle BoVW et que cette information est complémentaire aux méthodes de l'état de l'art. Ensuite, nous nous focalisons sur la description de l'information couleur. Contrairement aux approches traditionnelles qui s'appuient sur des descriptions invariantes aux changements d'éclairage, nous proposons un descripteur basé sur le pouvoir discriminant. Nos expérimentations permettent de conclure que ce descripteur apprend automatiquement un certain degré d'invariance photométrique tout en surclassant les descripteurs basés sur cette invariance photométrique. De plus, combiné avec un descripteur de forme, le descripteur proposé donne des résultats excellents sur quatre jeux de données particulièrement difficiles. Enfin, nous nous intéressons à la représentation de la couleur à partir de la réflectance multispectrale des surfaces observées, information difficile à extraire sans instruments sophistiqués. Ainsi, nous proposons d'utiliser l'écran et la caméra d'un appareil portable pour capturer des images éclairées par les couleurs primaires de l'écran. Trois éclairages et trois réponses de caméra produisent neuf valeurs pour estimer la réflectance. Les résultats montrent que la précision de la reconstruction spectrale est meilleure que celle estimée avec un seul éclairage. Nous concluons que ce type d'acquisition est possible avec des appareils grand public tels que les tablettes, téléphones ou ordinateurs portables / Image representation is in the heart of many computer vision algorithms. Different computer vision tasks (e.g. classification, detection) require discriminative image representations to recognize visual categories. In a nutshell, the bag-of-visual-words image representation is the most successful approach for object and scene recognition. In this thesis, we mainly revolve around this model and search for discriminative image representations. In the first part, we present a novel approach to incorporate spatial information in the BoVW method. In this framework, we present a simple and efficient way to infuse spatial information by taking advantage of the orientation and length of the segments formed by pairs of similar descriptors. We introduce the notion of soft-similarity to compute intra and inter visual word spatial relationships. We show experimentally that, our method adds important discriminative information to the BoVW method and complementary to the state-of-the-art method. Next, we focus on color description in general. Differing from traditional approaches of invariant description to account for photometric changes, we propose discriminative color descriptor. We demonstrate that such a color description automatically learns a certain degree of photometric invariance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape descriptor, the proposed color descriptor obtain excellent results on four challenging data sets.Finally, we focus on the most accurate color representation i.e. multispectral reflectance which is an intrinsic property of a surface. Even with the modern era technological advancement, it is difficult to extract reflectance information without sophisticated instruments. To this end, we propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results show that the accuracy of the spectral reconstruction improves significantly over the spectral reconstruction based on a single illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops

Image representation is in the heart of many computer vision algorithms. Different computer vision tasks (e.g. classification, detection) require discriminative image representations to recognize visual categories. In a nutshell, the bag-of-visual-words image representation is the most successful approach for object and scene recognition. In this thesis, we mainly revolve around this model and search for discriminative image representations. In the first part, we present a novel approach to incorporate spatial information in the BoVW method. In this framework, we present a simple and efficient way to infuse spatial information by taking advantage of the orientation and length of the segments formed by pairs of similar descriptors. We introduce the notion of soft-similarity to compute intra and inter visual word spatial relationships. We show experimentally that, our method adds important discriminative information to the BoVW method and complementary to the state-of-the-art method. Next, we focus on color description in general. Differing from traditional approaches of invariant description to account for photometric changes, we propose discriminative color descriptor. We demonstrate that such a color description automatically learns a certain degree of photometric invariance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape descriptor, the proposed color descriptor obtain excellent results on four challenging data sets.Finally, we focus on the most accurate color representation i.e. multispectral reflectance which is an intrinsic property of a surface. Even with the modern era technological advancement, it is difficult to extract reflectance information without sophisticated instruments. To this end, we propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results show that the accuracy of the spectral reconstruction improves significantly over the spectral reconstruction based on a single illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops

Traditionally, local shape descriptors or color and texture based descriptors are used to describe the content of images. Although, these solutions achieving good results, they are not able to distinguish scenes that contain objects with the same colors, but with a different spatial organization or do not supports partial matching. In this work we focus on a particular case of the partial matching that is to find individual objects in images that contain various objects. Since the color is one of the most visually distinguishable properties, we propose a new descriptor based only on color able to find pictures of objects that are contained in other images. Although our descriptor has shown better results when compared to related works, this new color descriptor is not able to discriminate objects topologically different but having the same colors. To overcome this problem, we also propose a new approach to the partial matching of images that combine color and topological features on a single descriptor. This new descriptor, first performs a simplification process of the original image, which identifies the color regions that make up the image. Then, we represent the spatial information among the color regions using a topological graph, where vertices represent the color regions and the edges represent the spatial connections between them. To calculate the descriptor from this graph representation, we use the spectral theory of graphs, avoiding the need to make a direct comparison between graphs. To support the partial matching, we propose a decomposition of the main graph into several subgraphs, and also calculate descriptors for these subgraphs. / Tradicionalmente, descritores de forma, ou descritores baseados em cor e textura, são utilizados para descrever o conteúdo visual das imagens. Embora essas abordagens apresentem bons resultados, elas não são capazes de diferenciar adequadamente imagens que contêm objetos com as mesmas cores, mas com organização espacial diferente ou não suportam a pesquisa parcial de imagens. Neste trabalho focamos em um caso particular da pesquisa parcial de imagens, que é encontrar objetos em imagens que contenham vários objetos, não deixando de lado a pesquisa total (encontrar imagens similares à original). Dado que a cor é uma das propriedades visuais mais discriminativas, propomos um novo descritor baseado somente em cor capaz de encontrar imagens de objetos que estão contidos em outras imagens. Embora tenha apresentado melhores resultados quando comparado a trabalhos correlatos, esse novo descritor de cor não é capaz de discriminar objetos topologicamente diferentes mas que possuam as mesmas cores. Com o intuito de resolver esse problema, também propomos uma nova abordagem para a recuperação parcial de imagens que combina características topológicas e de cor em um único descritor. Esse novo descritor primeiramente realiza um processo de simplificação da imagem original, onde são identificadas as regiões de cor que compõem a imagem. Após esse processo de simplificação, a organização espacial das regiões de cor previamente identificadas é representada por meio de um grafo topológico, onde os vértices representam as regiões de cor e as arestas representam as conexões entre essas regiões. O descritor topológico é então calculado a partir do grafo de topologia utilizando a teoria espectral de grafos, evitando a necessidade de se realizar uma comparação direta entre grafos. Para suportar a pesquisa parcial de imagens, é realizada uma decomposição do grafo principal em diversos subgrafos. / Mestre em Ciência da Computação