Unsupervised learning of generative topic saliency for person re-identification

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data

Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

Hierarchical representations for spatio-temporal visual attention: modeling and understanding

Mención Internacional en el título de doctorDentro del marco de la Inteligencia Artificial, la Visión Artificial es una disciplina científica que tiene como objetivo simular automaticamente las funciones del sistema visual humano, tratando de resolver tareas como la localización y el reconocimiento de objetos, la detección de eventos o el seguimiento de objetos....Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Salgado Álvarez de Sotomayor.- Secretario: Ascensión Gallardo Antolín.- Vocal: Jenny Benois Pinea

3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives. As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications. This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics

Generative topic modeling in image data mining and bioinformatics studies

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model is proposed for co-existing image features and annotations, in which new latent variables are introduced to allow for more flexible sampling of word topics and visual topics. A perspective hierarchical Dirichlet process (pHDP) model is proposed to deal with user-tagged image modeling, associating image features with image tags and incorporating the user’s perspectives into the image tag generation process. It’s also shown that in mining large scale text corpora of natural language descriptions, the relation between semantic visual attributes and object categories can be encoded as Must-Links and Cannot-Links, which can be represented by Dirichlet-Forest prior. Novel generative topic models are also introduced to meta-genomics studies. The experimental results show that the generative topic model can be used to model the taxon abundance information obtained by the homology-based approach and study the microbial core. It also shows that latent topic modeling can be used to characterize core and distributed genes within a species and to correlate similarities between genes and their functions. A further study on the functional elements derived from the non-redundant CDs catalogue shows that the configuration of functional groups encoded in the gene-expression data of meta-genome samples can be inferred by applying probabilistic topic modeling to functional elements. Furthermore, an extended HDP model is introduced to infer functional basis from detected enterotypes. The latent topics estimated from human gut microbial samples are evidenced by the recent discoveries in fecal microbiota study, which demonstrate the effectiveness of the proposed models.Ph.D., Information Systems -- Drexel University, 201

Visual object category discovery in images and videos

textThe current trend in visual recognition research is to place a strict division between the supervised and unsupervised learning paradigms, which is problematic for two main reasons. On the one hand, supervised methods require training data for each and every category that the system learns; training data may not always be available and is expensive to obtain. On the other hand, unsupervised methods must determine the optimal visual cues and distance metrics that distinguish one category from another to group images into semantically meaningful categories; however, for unlabeled data, these are unknown a priori. I propose a visual category discovery framework that transcends the two paradigms and learns accurate models with few labeled exemplars. The main insight is to automatically focus on the prevalent objects in images and videos, and learn models from them for category grouping, segmentation, and summarization. To implement this idea, I first present a context-aware category discovery framework that discovers novel categories by leveraging context from previously learned categories. I devise a novel object-graph descriptor to model the interaction between a set of known categories and the unknown to-be-discovered categories, and group regions that have similar appearance and similar object-graphs. I then present a collective segmentation framework that simultaneously discovers the segmentations and groupings of objects by leveraging the shared patterns in the unlabeled image collection. It discovers an ensemble of representative instances for each unknown category, and builds top-down models from them to refine the segmentation of the remaining instances. Finally, building on these techniques, I show how to produce compact visual summaries for first-person egocentric videos that focus on the important people and objects. The system leverages novel egocentric and high-level saliency features to predict important regions in the video, and produces a concise visual summary that is driven by those regions. I compare against existing state-of-the-art methods for category discovery and segmentation on several challenging benchmark datasets. I demonstrate that we can discover visual concepts more accurately by focusing on the prevalent objects in images and videos, and show clear advantages of departing from the status quo division between the supervised and unsupervised learning paradigms. The main impact of my thesis is that it lays the groundwork for building large-scale visual discovery systems that can automatically discover visual concepts with minimal human supervision.Electrical and Computer Engineerin

Representation and recognition of human actions in video

PhDAutomated human action recognition plays a critical role in the development of human-machine communication, by aiming for a more natural interaction between artificial intelligence and the human society. Recent developments in technology have permitted a shift from a traditional human action recognition performed in a well-constrained laboratory environment to realistic unconstrained scenarios. This advancement has given rise to new problems and challenges still not addressed by the available methods. Thus, the aim of this thesis is to study innovative approaches that address the challenging problems of human action recognition from video captured in unconstrained scenarios. To this end, novel action representations, feature selection methods, fusion strategies and classification approaches are formulated. More specifically, a novel interest points based action representation is firstly introduced, this representation seeks to describe actions as clouds of interest points accumulated at different temporal scales. The idea behind this method consists of extracting holistic features from the point clouds and explicitly and globally describing the spatial and temporal action dynamic. Since the proposed clouds of points representation exploits alternative and complementary information compared to the conventional interest points-based methods, a more solid representation is then obtained by fusing the two representations, adopting a Multiple Kernel Learning strategy. The validity of the proposed approach in recognising action from a well-known benchmark dataset is demonstrated as well as the superior performance achieved by fusing representations. Since the proposed method appears limited by the presence of a dynamic background and fast camera movements, a novel trajectory-based representation is formulated. Different from interest points, trajectories can simultaneously retain motion and appearance information even in noisy and crowded scenarios. Additionally, they can handle drastic camera movements and a robust region of interest estimation. An equally important contribution is the proposed collaborative feature selection performed to remove redundant and noisy components. In particular, a novel feature selection method based on Multi-Class Delta Latent Dirichlet Allocation (MC-DLDA) is introduced. Crucial, to enrich the final action representation, the trajectory representation is adaptively fused with a conventional interest point representation. The proposed approach is extensively validated on different datasets, and the reported performances are comparable with the best state-of-the-art. The obtained results also confirm the fundamental contribution of both collaborative feature selection and adaptive fusion. Finally, the problem of realistic human action classification in very ambiguous scenarios is taken into account. In these circumstances, standard feature selection methods and multi-class classifiers appear inadequate due to: sparse training set, high intra-class variation and inter-class similarity. Thus, both the feature selection and classification problems need to be redesigned. The proposed idea is to iteratively decompose the classification task in subtasks and select the optimal feature set and classifier in accordance with the subtask context. To this end, a cascaded feature selection and action classification approach is introduced. The proposed cascade aims to classify actions by exploiting as much information as possible, and at the same time trying to simplify the multi-class classification in a cascade of binary separations. Specifically, instead of separating multiple action classes simultaneously, the overall task is automatically divided into easier binary sub-tasks. Experiments have been carried out using challenging public datasets; the obtained results demonstrate that with identical action representation, the cascaded classifier significantly outperforms standard multi-class classifiers