Early Active Learning with Pairwise Constraint for Person Re-identification

© 2017, Springer International Publishing AG. Research on person re-identification (re-id) has attached much attention in the machine learning field in recent years. With sufficient labeled training data, supervised re-id algorithm can obtain promising performance. However, producing labeled data for training supervised re-id models is an extremely challenging and time-consuming task because it requires every pair of images across no-overlapping camera views to be labeled. Moreover, in the early stage of experiments, when labor resources are limited, only a small number of data can be labeled. Thus, it is essential to design an effective algorithm to select the most representative samples. This is referred as early active learning or early stage experimental design problem. The pairwise relationship plays a vital role in the re-id problem, but most of the existing early active learning algorithms fail to consider this relationship. To overcome this limitation, we propose a novel and efficient early active learning algorithm with a pairwise constraint for person re-identification in this paper. By introducing the pairwise constraint, the closeness of similar representations of instances is enforced in active learning. This benefits the performance of active learning for re-id. Extensive experimental results on four benchmark datasets confirm the superiority of the proposed algorithm

Re-identificación de personas

En la actualidad, la re-identificación de personas es un recurso con una alta demanda sobre todo en el ámbito de la video seguridad, esto no significa conocer la identidad de una persona sino poder hacer un seguimiento de esta en distintas cámaras cuyas imágenes no se solapen. Hay una gran cantidad de medidas de evaluación tradicionales que nos permiten hacer extracciones” manuales” de características, las cuales utilizan algoritmos matemáticos los cuales permiten extraer información de las imágenes. Sin embargo, hay gran cantidad de aspectos a tener en cuenta si queremos que nuestros modelos funcionen lo mejor posible, como puede ser la orientación de la persona, el entorno o su posición. La extracción de características basada en aprendizaje profundo realiza un modelado de datos, para ello utiliza flujos de datos de gran tamaño para aprender de estos y poder realizar una clasificación y un análisis predictivo. El aprendizaje profundo se basa en la utilización de redes neuronales, un modelo matemático que trata de imitar el comportamiento biológico de las neuronas, conectando diferentes capas de procesamiento y otorgando distintos pesos a cada una con el fin de obtener un modelo optimizado. El objetivo de este TFG es la comparación de los resultados de las medidas de extracción manuales (handcrafted features) y las automáticas (basadas en Deep Learning), esto se realizará ejecutando un script que nos calcule el porcentaje de acierto a la hora de re-identificar personas entre las cámaras utilizando los distintos métodos de extracción manual y después utilizando los basados en redes neuronales en los datasets que utilicemos para evaluar

Scalable learning for geostatistics and speaker recognition

With improved data acquisition methods, the amount of data that is being collected has increased severalfold. One of the objectives in data collection is to learn useful underlying patterns. In order to work with data at this scale, the methods not only need to be effective with the underlying data, but also have to be scalable to handle larger data collections. This thesis focuses on developing scalable and effective methods targeted towards different domains, geostatistics and speaker recognition in particular. Initially we focus on kernel based learning methods and develop a GPU based parallel framework for this class of problems. An improved numerical algorithm that utilizes the GPU parallelization to further enhance the computational performance of kernel regression is proposed. These methods are then demonstrated on problems arising in geostatistics and speaker recognition. In geostatistics, data is often collected at scattered locations and factors like instrument malfunctioning lead to missing observations. Applications often require the ability interpolate this scattered spatiotemporal data on to a regular grid continuously over time. This problem can be formulated as a regression problem, and one of the most popular geostatistical interpolation techniques, kriging is analogous to a standard kernel method: Gaussian process regression. Kriging is computationally expensive and needs major modifications and accelerations in order to be used practically. The GPU framework developed for kernel methods is extended to kriging and further the GPU's texture memory is better utilized for enhanced computational performance. Speaker recognition deals with the task of verifying a person's identity based on samples of his/her speech - "utterances". This thesis focuses on text-independent framework and three new recognition frameworks were developed for this problem. We proposed a kernelized Renyi distance based similarity scoring for speaker recognition. While its performance is promising, it does not generalize well for limited training data and therefore does not compare well to state-of-the-art recognition systems. These systems compensate for the variability in the speech data due to the message, channel variability, noise and reverberation. State-of-the-art systems model each speaker as a mixture of Gaussians (GMM) and compensate for the variability (termed "nuisance"). We propose a novel discriminative framework using a latent variable technique, partial least squares (PLS), for improved recognition. The kernelized version of this algorithm is used to achieve a state of the art speaker ID system, that shows results competitive with the best systems reported on in NIST's 2010 Speaker Recognition Evaluation