Normalized Cuts and Image Segmentation

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging

Activity representation with motion hierarchies

International audienceComplex activities, e.g., pole vaulting, are composed of a variable number of sub-events connected by complex spatio-temporal relations, whereas simple actions can be represented as sequences of short temporal parts. In this paper, we learn hierarchical representations of activity videos in an unsupervised manner. These hierarchies of mid-level motion components are data-driven decompositions specific to each video. We introduce a spectral divisive clustering algorithm to efficiently extract a hierarchy over a large number of tracklets (i.e., local trajectories). We use this structure to represent a video as an unordered binary tree. We model this tree using nested histograms of local motion features. We provide an efficient positive definite kernel that computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent-child relations. We present experimental results on four recent challenging benchmarks: the High Five dataset [Patron-Perez et al, 2010], the Olympics Sports dataset [Niebles et al, 2010], the Hollywood 2 dataset [Marszalek et al, 2009], and the HMDB dataset [Kuehne et al, 2011]. We show that pervideo hierarchies provide additional information for activity recognition. Our approach improves over unstructured activity models, baselines using other motion decomposition algorithms, and the state of the art

Uma abordagem de agrupamento baseada na técnica de divisão e conquista e floresta de caminhos ótimos

Orientador: Alexandre Xavier FalcãoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O agrupamento de dados é um dos principais desafios em problemas de Ciência de Dados. Apesar do seu progresso científico em quase um século de existência, algoritmos de agrupamento ainda falham na identificação de grupos (clusters) naturalmente relacionados com a semântica do problema. Ademais, os avanços das tecnologias de aquisição, comunicação, e armazenamento de dados acrescentam desafios cruciais com o aumento considerável de dados, os quais não são tratados pela maioria das técnicas. Essas questões são endereçadas neste trabalho através da proposta de uma abordagem de divisão e conquista para uma técnica de agrupamento única em encontrar um grupo por domo da função de densidade de probabilidade dos dados --- o algoritmo de agrupamento por floresta de caminhos ótimos (OPF - Optimum-Path Forest). Nesta técnica, amostras são interpretadas como nós de um grafo cujos arcos conectam os $k$-vizinhos mais próximos no espaço de características. Os nós são ponderados pela sua densidade de probabilidade e um mapa de conexidade é maximizado de modo que cada máximo da função densidade de probabilidade se torna a raiz de uma árvore de caminhos ótimos (grupo). O melhor valor de $k$ é estimado por otimização em um intervalo de valores dependente da aplicação. O problema com este método é que um número alto de amostras torna o algoritmo inviável, devido ao espaço de memória necessário para armazenar o grafo e o tempo computacional para encontrar o melhor valor de $k$. Visto que as soluções existentes levam a resultados ineficazes, este trabalho revisita o problema através da proposta de uma abordagem de divisão e conquista com dois níveis. No primeiro nível, o conjunto de dados é dividido em subconjuntos (blocos) menores e as amostras pertencentes a cada bloco são agrupadas pelo algoritmo OPF. Em seguida, as amostras representativas de cada grupo (mais especificamente as raízes da floresta de caminhos ótimos) são levadas ao segundo nível, onde elas são agrupadas novamente. Finalmente, os rótulos de grupo obtidos no segundo nível são transferidos para todas as amostras do conjunto de dados através de seus representantes do primeiro nível. Nesta abordagem, todas as amostras, ou pelo menos muitas delas, podem ser usadas no processo de aprendizado não supervisionado, sem afetar a eficácia do agrupamento e, portanto, o procedimento é menos susceptível a perda de informação relevante ao agrupamento. Os resultados mostram agrupamentos satisfatórios em dois cenários, segmentação de imagem e agrupamento de dados arbitrários, tendo como base a comparação com abordagens populares. No primeiro cenário, a abordagem proposta atinge os melhores resultados em todas as bases de imagem testadas. No segundo cenário, os resultados são similares aos obtidos por uma versão otimizada do método original de agrupamento por floresta de caminhos ótimosAbstract: Data clustering is one of the main challenges when solving Data Science problems. Despite its progress over almost one century of research, clustering algorithms still fail in identifying groups naturally related to the semantics of the problem. Moreover, the advances in data acquisition, communication, and storage technologies add crucial challenges with a considerable data increase, which are not handled by most techniques. We address these issues by proposing a divide-and-conquer approach to a clustering technique, which is unique in finding one group per dome of the probability density function of the data --- the Optimum-Path Forest (OPF) clustering algorithm. In the OPF-clustering technique, samples are taken as nodes of a graph whose arcs connect the $k$-nearest neighbors in the feature space. The nodes are weighted by their probability density values and a connectivity map is maximized such that each maximum of the probability density function becomes the root of an optimum-path tree (cluster). The best value of $k$ is estimated by optimization within an application-specific interval of values. The problem with this method is that a high number of samples makes the algorithm prohibitive, due to the required memory space to store the graph and the computational time to obtain the clusters for the best value of $k$. Since the existing solutions lead to ineffective results, we decided to revisit the problem by proposing a two-level divide-and-conquer approach. At the first level, the dataset is divided into smaller subsets (blocks) and the samples belonging to each block are grouped by the OPF algorithm. Then, the representative samples (more specifically the roots of the optimum-path forest) are taken to a second level where they are clustered again. Finally, the group labels obtained in the second level are transferred to all samples of the dataset through their representatives of the first level. With this approach, we can use all samples, or at least many samples, in the unsupervised learning process without affecting the grouping performance and, therefore, the procedure is less likely to lose relevant grouping information. We show that our proposal can obtain satisfactory results in two scenarios, image segmentation and the general data clustering problem, in comparison with some popular baselines. In the first scenario, our technique achieves better results than the others in all tested image databases. In the second scenario, it obtains outcomes similar to an optimized version of the traditional OPF-clustering algorithmMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

A Quorum Sensing Inspired Algorithm for Dynamic Clustering

Quorum sensing is a decentralized biological process, through which a community of cells with no global awareness coordinate their functional behaviors based solely on cell-medium interactions and local decisions. This paper draws inspirations from quorum sensing and colony competition to derive a new algorithm for data clustering. The algorithm treats each data as a single cell, and uses knowledge of local connectivity to cluster cells into multiple colonies simultaneously. It simulates auto-inducers secretion in quorum sensing to tune the influence radius for each cell. At the same time, sparsely distributed core cells spread their influences to form colonies, and interactions between colonies eventually determine each cell's identity. The algorithm has the flexibility to analyze not only static but also time-varying data, which surpasses the capacity of many existing algorithms. Its stability and convergence properties are established. The algorithm is tested on several applications, including both synthetic and real benchmarks data sets, alleles clustering, community detection, image segmentation. In particular, the algorithm's distinctive capability to deal with time-varying data allows us to experiment it on novel applications such as robotic swarms grouping and switching model identification. We believe that the algorithm's promising performance would stimulate many more exciting applications

Motion Segmentation from Clustering of Sparse Point Features Using Spatially Constrained Mixture Models

Motion is one of the strongest cues available for segmentation. While motion segmentation finds wide ranging applications in object detection, tracking, surveillance, robotics, image and video compression, scene reconstruction, video editing, and so on, it faces various challenges such as accurate motion recovery from noisy data, varying complexity of the models required to describe the computed image motion, the dynamic nature of the scene that may include a large number of independently moving objects undergoing occlusions, and the need to make high-level decisions while dealing with long image sequences. Keeping the sparse point features as the pivotal point, this thesis presents three distinct approaches that address some of the above mentioned motion segmentation challenges. The first part deals with the detection and tracking of sparse point features in image sequences. A framework is proposed where point features can be tracked jointly. Traditionally, sparse features have been tracked independently of one another. Combining the ideas from Lucas-Kanade and Horn-Schunck, this thesis presents a technique in which the estimated motion of a feature is influenced by the motion of the neighboring features. The joint feature tracking algorithm leads to an improved tracking performance over the standard Lucas-Kanade based tracking approach, especially while tracking features in untextured regions. The second part is related to motion segmentation using sparse point feature trajectories. The approach utilizes a spatially constrained mixture model framework and a greedy EM algorithm to group point features. In contrast to previous work, the algorithm is incremental in nature and allows for an arbitrary number of objects traveling at different relative speeds to be segmented, thus eliminating the need for an explicit initialization of the number of groups. The primary parameter used by the algorithm is the amount of evidence that must be accumulated before the features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. The approach works in real time and is able to segment various challenging sequences captured from still and moving cameras that contain multiple independently moving objects and motion blur. The third part of this thesis deals with the use of specialized models for motion segmentation. The articulated human motion is chosen as a representative example that requires a complex model to be accurately described. A motion-based approach for segmentation, tracking, and pose estimation of articulated bodies is presented. The human body is represented using the trajectories of a number of sparse points. A novel motion descriptor encodes the spatial relationships of the motion vectors representing various parts of the person and can discriminate between articulated and non-articulated motions, as well as between various pose and view angles. Furthermore, a nearest neighbor search for the closest motion descriptor from the labeled training data consisting of the human gait cycle in multiple views is performed, and this distance is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints and scale demonstrate the effectiveness of the approach. In particular, the purely motion based approach is able to track people in night-time sequences, even when the appearance based cues are not available. Finally, an application of image segmentation is presented in the context of iris segmentation. Iris is a widely used biometric for recognition and is known to be highly accurate if the segmentation of the iris region is near perfect. Non-ideal situations arise when the iris undergoes occlusion by eyelashes or eyelids, or the overall quality of the segmented iris is affected by illumination changes, or due to out-of-plane rotation of the eye. The proposed iris segmentation approach combines the appearance and the geometry of the eye to segment iris regions from non-ideal images. The image is modeled as a Markov random field, and a graph cuts based energy minimization algorithm is applied to label the pixels either as eyelashes, pupil, iris, or background using texture and image intensity information. The iris shape is modeled as an ellipse and is used to refine the pixel based segmentation. The results indicate the effectiveness of the segmentation algorithm in handling non-ideal iris images

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

Superpixel lattices

Superpixels are small image segments that are used in popular approaches to object detection and recognition problems. The superpixel approach is motivated by the observation that pixels within small image segments can usually be attributed the same label. This allows a superpixel representation to produce discriminative features based on data dependent regions of support. The reduced set of image primitives produced by superpixels can also be exploited to improve the efficiency of subsequent processing steps. However, it is common for the superpixel representation to have a different graph structure from the original pixel representation of the image. The first part of the thesis argues that a number of desirable properties of the pixel representation should be maintained by superpixels and that this is not possible with existing methods. We propose a new representation, the superpixel lattice, and demonstrate its advantages. The second part of the thesis investigates incorporating a priori information into superpixel segmentations. We learn a probabilistic model that describes the spatial density of object boundaries in the image. We demonstrate our approach using road scene data and show that our algorithm successfully exploits the spatial distribution of object boundaries to improve the superpixel segmentation. The third part of the thesis presents a globally optimal solution to our superpixel lattice problem in either the horizontal or vertical direction. The solution makes use of a Markov Random Field formulation where the label field is guaranteed to be a set of ordered layers. We introduce an iterative algorithm that uses this framework to learn colour distributions across an image in an unsupervised manner. We conclude that our approach achieves comparable or better performance than competing methods and that it confers several additional advantages