A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM)

Discovering Multi-relational Latent Attributes by Visual Similarity Networks

Abstract. The key problems in visual object classification are: learning discriminative feature to distinguish between two or more visually similar categories ( e.g. dogs and cats), modeling the variation of visual appear-ance within instances of the same class (e.g. Dalmatian and Chihuahua in the same category of dogs), and tolerate imaging distortion (3D pose). These account to within and between class variance in machine learning terminology, but in recent works these additional pieces of information, latent dependency, have been shown to be beneficial for the learning process. Latent attribute space was recently proposed and verified to capture the latent dependent correlation between classes. Attributes can be annotated manually, but more attempting is to extract them in an unsupervised manner. Clustering is one of the popular unsupervised ap-proaches, and the recent literature introduces similarity measures that help to discover visual attributes by clustering. However, the latent at-tribute structure in real life is multi-relational, e.g. two different sport cars in different poses vs. a sport car and a family car in the same pose-what attribute can dominate similarity? Instead of clustering, a network (graph) containing multiple connections is a natural way to represent such multi-relational attributes between images. In the light of this, we introduce an unsupervised framework for network construction based on pairwise visual similarities and experimentally demonstrate that the constructed network can be used to automatically discover multiple dis-crete (e.g. sub-classes) and continuous (pose change) latent attributes. Illustrative examples with publicly benchmarking datasets can verify the effectiveness of capturing multi- relation between images in the unsuper-vised style by our proposed network.

Contributions to region-based image and video analysis: feature aggregation, background subtraction and description constraining

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 22-01-2016Esta tesis tiene embargado el acceso al texto completo hasta el 22-07-2017The use of regions for image and video analysis has been traditionally motivated by their ability to diminish the number of processed units and hence, the number of required decisions. However, as we explore in this thesis, this is just one of the potential advantages that regions may provide. When dealing with regions, two description spaces may be differentiated: the decision space, on which regions are shaped—region segmentation—, and the feature space, on which regions are used for analysis—region-based applications—. These two spaces are highly related. The solutions taken on the decision space severely affect their performance in the feature space. Accordingly, in this thesis we propose contributions on both spaces. Regarding the contributions to region segmentation, these are two-fold. Firstly, we give a twist to a classical region segmentation technique, the Mean-Shift, by exploring new solutions to automatically set the spectral kernel bandwidth. Secondly, we propose a method to describe the micro-texture of a pixel neighbourhood by using an easily customisable filter-bank methodology—which is based on the discrete cosine transform (DCT)—. The rest of the thesis is devoted to describe region-based approaches to several highly topical issues in computer vision; two broad tasks are explored: background subtraction (BS) and local descriptors (LD). Concerning BS, regions are here used as complementary cues to refine pixel-based BS algorithms: by providing robust to illumination cues and by storing the background dynamics in a region-driven background modelling. Relating to LD, the region is here used to reshape the description area usually fixed for local descriptors. Region-masked versions of classical two-dimensional and three-dimensional local descriptions are designed. So-built descriptions are proposed for the task of object identification, under a novel neural-oriented strategy. Furthermore, a local description scheme based on a fuzzy use of the region membership is derived. This characterisation scheme has been geometrically adapted to account for projective deformations, providing a suitable tool for finding corresponding points in wide-baseline scenarios. Experiments have been conducted for every contribution, discussing the potential benefits and the limitations of the proposed schemes. In overall, obtained results suggest that the region—conditioned by successful aggregation processes—is a reliable and useful tool to extrapolate pixel-level results, diminish semantic noise, isolate significant object cues and constrain local descriptions. The methods and approaches described along this thesis present alternative or complementary solutions to pixel-based image processing.El uso de regiones para el análisis de imágenes y secuencias de video ha estado tradicionalmente motivado por su utilidad para disminuir el número de unidades de análisis y, por ende, el número de decisiones. En esta tesis evidenciamos que esta es sólo una de las muchas ventajas adheridas a la utilización de regiones. En el procesamiento por regiones deben distinguirse dos espacios de análisis: el espacio de decisión, en donde se construyen las regiones, y el espacio de características, donde se utilizan. Ambos espacios están altamente relacionados. Las soluciones diseñadas para la construcción de regiones en el espacio de decisión definen su utilidad en el espacio de análisis. Por este motivo, a lo largo de esta tesis estudiamos ambos espacios. En particular, proponemos dos contribuciones en la etapa de construcción de regiones. En la primera, revisitamos una técnica clásica, Mean-Shift, e introducimos un esquema para la selección automática del ancho de banda que permite estimar localmente la densidad de una determinada característica. En la segunda, utilizamos la transformada discreta del coseno para describir la variabilidad local en el entorno de un píxel. En el resto de la tesis exploramos soluciones en el espacio de características, en otras palabras, proponemos aplicaciones que se apoyan en la región para realizar el procesamiento. Dichas aplicaciones se centran en dos ramas candentes en el ámbito de la visión por computador: la segregación del frente por substracción del fondo y la descripción local de los puntos de una imagen. En la rama substracción de fondo, utilizamos las regiones como unidades de apoyo a los algoritmos basados exclusivamente en el análisis a nivel de píxel. En particular, mejoramos la robustez de estos algoritmos a los cambios locales de iluminación y al dinamismo del fondo. Para esta última técnica definimos un modelo de fondo completamente basado en regiones. Las contribuciones asociadas a la rama de descripción local están centradas en el uso de la región para definir, automáticamente, entornos de descripción alrededor de los puntos. En las aproximaciones existentes, estos entornos de descripción suelen ser de tamaño y forma fija. Como resultado de este procedimiento se establece el diseño de versiones enmascaradas de descriptores bidimensionales y tridimensionales. En el algoritmo desarrollado, organizamos los descriptores así diseñados en una estructura neuronal y los utilizamos para la identificación automática de objetos. Por otro lado, proponemos un esquema de descripción mediante asociación difusa de píxeles a regiones. Este entorno de descripción es transformado geométricamente para adaptarse a potenciales deformaciones proyectivas en entornos estéreo donde las cámaras están ampliamente separadas. Cada una de las aproximaciones desarrolladas se evalúa y discute, remarcando las ventajas e inconvenientes asociadas a su utilización. En general, los resultados obtenidos sugieren que la región, asumiendo que ha sido construida de manera exitosa, es una herramienta fiable y de utilidad para: extrapolar resultados a nivel de pixel, reducir el ruido semántico, aislar las características significativas de los objetos y restringir la descripción local de estas características. Los métodos y enfoques descritos a lo largo de esta tesis establecen soluciones alternativas o complementarias al análisis a nivel de píxelIt was partially supported by the Spanish Government trough its FPU grant program and the projects (TEC2007-65400 - SemanticVideo), (TEC2011-25995 Event Video) and (TEC2014-53176-R HAVideo); the European Commission (IST-FP6-027685 - Mesh); the Comunidad de Madrid (S-0505/TIC-0223 - ProMultiDis-CM) and the Spanish Administration Agency CENIT 2007-1007 (VISION)