Unsupervised Detection of Emergent Patterns in Large Image Collections

With the advent of modern image acquisition and sharing technologies, billions of images are added to the Internet every day. This huge repository contains useful information, but it is very hard to analyze. If labeled information is available for this data, then supervised learning techniques can be used to extract useful information. Visual pattern mining approaches provide a way to discover visual structures and patterns in an image collection without the need of any supervision. The Internet contains images of various objects, scenes, patterns, and shapes. The majority of approaches for visual pattern discovery, on the other hand, find patterns that are related to object or scene categories.Emergent pattern mining techniques provide a way to extract generic, complex and hidden structures in images. This thesis describes research, experiments, and analysis conducted to explore various approaches to mine emergent patterns from image collections in an unsupervised way. These approaches are based on itemset mining and graph theoretic strategies. The itemset mining strategy uses frequent itemset mining and rare itemset mining techniques to discover patterns.The mining is performed on a transactional dataset which is obtained from the BoW representation of images. The graph-based approach represents visual word co-occurrences obtained from images in a co-occurrence graph.Emergent patterns form dense clusters in this graph that are extracted using normalized cuts. The patterns that are discovered using itemset mining approaches are:stripes and parallel lines;dots and checks;bright dots;single lines;intersections; and frames. The graph based approach revealed various interesting patterns, including some patterns that are related to object categories

Multimodal information spaces for content-based image retrieval

Abstract. Image collections today are increasingly larger in size, and they continue to grow constantly. Without the help of image search systems these abundant visual records collected in many different fields and domains may remain unused and inaccessible. Many available image databases often contain complementary modalities, such as attached text resources, which can be used to build an index for querying with keywords. However, sometimes users do not have or do not know the right words to express what they need, and, in addition, keywords do not express all the visual variations that an image may contain. Using example images as queries can be viewed as an alternative in different scenarios such as searching images using a mobile phone with a coupled camera, or supporting medical diagnosis by searching a large medical image collection. Still, matching only visual features between the query and image databases may lead to undesirable results from the user's perspective. These conditions make the process of finding relevant images for a specific information need very challenging, time consuming or even frustrating. Instead of considering only a single data modality to build image search indexes, the simultaneous use of both, visual and text data modalities, has been suggested. Non-visual information modalities may provide complementary information to enrich the image representation. The goal of this research work is to study the relationships between visual contents and text terms to build useful indexes for image search. A family of algorithms based on matrix factorization are proposed for extracting the multimodal aspects from an image collection. Using this knowledge about how visual features and text terms correlate, a search index is constructed, which can be searched using keywords, example images or combinations of both. Systematic experiments were conducted on different data sets to evaluate the proposed indexing algorithms. The experimental results showed that multimodal indexing is an effective strategy for designing image search systems.Las colecciones de imágenes hoy en día son muy grandes y crecen constantemente. Sin la ayuda de sistemas para la búsqueda de imágenes esos abundantes registros visuales que han sido recolectados en diferentes areas del conocimiento pueden permanecer aislados sin uso. Muchas bases de datos de imágenes contienen modalidades de datos complementarias, como los recursos textuales que pueden ser utilizados para crear índices de búsqueda. Sin embargo, algunas veces los usuarios no tienen o no saben qué palabras utilizar para encontrar lo que necesitan, y adicionalmente, las palabras clave no expresan todas las variaciones visuales que una imagen puede tener. Utilizar imágenes de ejemplo para expresar la consulta puede ser visto como una alternativa, por ejemplo buscar imágenes con teléfonos móviles, o dar soporte al diagnóstico médico con las imágenes de los pacientes. Aún así, emparejar correctamente las características visuales de la consulta y las imágenes en la base de datos puede llevar a resultados semánticamente incorrectos. Estas condiciones hacen que el proceso de buscar imágenes relevantes para una necesidad de información particular sea una tarea difícil, que consume mucho tiempo o que incluso puede ser frustrante. En lugar de considerar solo una modalidad de datos para construir índices de búsqueda para imágenes, el uso simultáneo de las modalidades visual y textual ha sido sugerido. Las modalidades no visuales pueden proporcionar información complementaria para enriquecer la representación de las imágenes. El objetivo de este trabajo de investigación es estudiar las relaciones entre los contenidos visuales y los términos textuales, para construir índices de búsqueda útiles. Este trabajo propone una familia de algoritmos basados en factorización de matrices para extraer los aspectos multimodales de una colección de imágenes. Utilizando este conocimiento acerca de cómo las características visuales se correlacionan con los términos textuales, se construye un índice que puede ser consultado con palabras clave, imágenes de ejemplo o por combinaciones de estas dos. Se realizaron experimentos sistemáticos en diferentes conjuntos de datos para evaluar los algoritmos de indexamiento propuestos. Los resultados muestran que el indexamiento multimodal es una estrategia efectiva para diseñar sistemas de búsqueda de imágenes.Doctorad