Color Textured Image Segmentation Based on Spatial Dependence Using 3D Co-occurrence Matrices and Markov Random Fields

Image segmentation is a primary step in many computer vision tasks. Although many segmentation methods based on either color or texture have been proposed in the last decades, there have been only few approaches combining both these features. This work presents a new image segmentation method using color texture features extracted from 3D co-occurrence matrices combined with spatial dependence, this modeled by a Markov random field. The 3D co-occurrence matrices provide features which summarize statistical interaction both between pixels and different color bands, which is not usually accomplished by other segmentation methods. After a preliminary segmentation of the image into homogeneous regions, the ICM method is applied only to pixels located in the boundaries between regions, providing a fine segmentation with a reduced computational cost, since a small portion of the image is considered in the last stage. A set of synthetic and natural color images is used to show the results by applying the proposed method

Open-set Face Recognition using Ensembles trained on Clustered Data

Open-set face recognition describes a scenario where unknown subjects, unseen during the training stage, appear on test time. Not only it requires methods that accurately identify individuals of interest, but also demands approaches that effectively deal with unfamiliar faces. This work details a scalable open-set face identification approach to galleries composed of hundreds and thousands of subjects. It is composed of clustering and an ensemble of binary learning algorithms that estimates when query face samples belong to the face gallery and then retrieves their correct identity. The approach selects the most suitable gallery subjects and uses the ensemble to improve prediction performance. We carry out experiments on well-known LFW and YTF benchmarks. Results show that competitive performance can be achieved even when targeting scalability.Comment: [Original paper title: Unconstrained Face Identification using Ensembles trained on Clustered Data] [2020 IEEE International Joint Conference on Biometrics (IJCB)] [https://ieeexplore.ieee.org/document/9304882

Segmentaçao de imagens baseada em dependencia espacial utilizando campo aleatório de Markov associado com características de texturas

Orientador: Hélio PedriniDissertaçao (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduaçao em Informática. Defesa: Curitiba, 2005Inclui bibliografiaResumo: Uma etapa crítica presente no processo de análise de imagens é a segmentação, responsável por obter informações de alto n'nível sobre as regiões ou objetos contidos na imagem, de modo a facilitar sua interpretação. Contudo, a segmentação ainda é um dos maiores desafios na área de análise de imagens, particularmente quando não se utiliza informações previamente adquiridas sobre a imagem a ser segmentada. Os métodos convencionais de segmentação desconsideram a dependência espacial entre as regiões, o que pode gerar resultados impróprios. Técnicas que consideram a dependência espacial entre as regiões da imagem têm recebido crescente atenção da comunidade científica, pois apresentam uma maior precisão nos resultados obtidos. Embora avanços significativos tenham sido alcançados na segmentação de texturas e de imagens coloridas separadamente, a combinação dessas duas propriedades é considerada como um problema bem mais complexo. Devido a importância dessa etapa no processo de análise de imagens e ao fato de não existirem soluções definitivas para o problema, este trabalho propõe o desenvolvimento de um novo método de segmentação aplicado a imagens texturizadas monocromáticas e coloridas. O método utiliza a formulação Bayesiana para associar a dependência espacial modelada por um campo aleatório de Markov com características de texturas. A segmentação final é obtida por meio da aplicação de t'cênicas de relaxação para minimizar uma função de energia definida a partir da referida associação. Experimentos são efetuados visando avaliar os métodos de análise de texturas, bem como validar a metodologia proposta.Abstract: A critical stage present in the image analysis process is the segmentation, responsible for obtaining high level information about regions or objects in the image, in order to facilitate its interpretation. However, the segmentation is still one of the greatest challenges in the image analysis area, particularly when it does not use information previously acquired on the image to be segmented. Conventional segmentation methods do not consider the spatial dependence between the regions, which can generate improper results. Techniques considering the spatial dependence between the image regions have received increasing attention from the scientific community, because they present a major precision in the obtained results. Although significant advances have been reached in the segmentation of textures and colored images separately, the combination of these two properties is considered a more complex problem. Due to the importance of this stage in the image analysis process and to the fact that does not exist definitive solutions to the problem, this work considers the development of a new segmentation method applied to gray scale and color texture images. The method uses the Bayesian formulation to associate the spatial dependence modeled by a Markov random field with texture features. The final segmentation is obtained by the application of relaxation techniques to minimize an energy function defined by such association. Experiments are performed to evaluate the texture analysis methods, as well as validating the proposal method

Activity Recognition based on a Magnitude-Orientation Stream Network

The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.Comment: 8 pages, SIBGRAPI 201

Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation

Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects. Therefore, they are expected to prevent face samples of unregistered subjects from being identified as previously enrolled identities. This watchlist context adds an arduous requirement that calls for the dismissal of irrelevant faces by focusing mainly on subjects of interest. As a response, this work introduces a novel method that associates an ensemble of compact neural networks with a margin-based cost function that explores additional samples. Supplementary negative samples can be obtained from external databases or synthetically built at the representation level in training time with a new mix-up feature augmentation approach. Deep neural networks pre-trained on large face datasets serve as the preliminary feature extraction module. We carry out experiments on well-known LFW and IJB-C datasets where results show that the approach is able to boost closed and open-set identification rates