Analysis of Using Metric Access Methods for Visual Search of Objects in Video Databases

This article presents an approach to object retrieval that searches for and localizes all the occurrences of an object in a video database, given a query image of the object. Our proposal is based on text-retrieval methods in which video key frames are represented by a dense set of viewpoint invariant region descriptors that enable recognition to proceed successfully despite changes in camera viewpoint, lighting, and partial occlusions. Vector quantizing these region descriptors provides a visual analogy of a word - a visual word. Those words are grouped into a visual vocabulary which is used to index all key frames from the video database. Efficient retrieval is then achieved by employing methods from statistical text retrieval, including inverted file systems, and text-document frequency weightings. Though works in the literature have only adopted a simple sequential scan during search, we investigate the use of different metric access methods (MAM): M-tree, Slim-tree, and D-index, in order to accelerate the processing of similarity queries. In addition, a ranking strategy based on the spatial layout of the regions (spatial consistency) is fully described and evaluated. Experimental results have shown that the adoption of MAMs not only has improved the search performance but also has reduced the influence of the vocabulary size over test results, which may improve the scalability of our proposal. Finally, the application of spatial consistency has produced a very significant improvement of the results

Aprendizado de representações e correspondências baseadas em grafos para tarefas de classificação

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Muitas situações do mundo real podem ser modeladas por meio de objetos e seus relacionamentos, como, por exemplo, estradas conectando cidades em um mapa. Grafo é um conceito derivado da abstração dessas situações. Grafos são uma poderosa representação estrutural que codifica relações entre objetos e entre seus componentes em um único formalismo. Essa representação é tão poderosa que é aplicada em uma ampla gama de aplicações, de bioinformática a redes sociais. Dessa maneira, diversos problemas de reconhecimento de padrões são modelados para utilizar representações baseadas em grafos. Em problemas de classificação, os relacionamentos presentes entre objetos ou entre seus componentes são explorados para obter soluções efetivas e/ou eficientes. Nesta tese, nós investigamos o uso de grafos em problemas de classificação. Nós propomos duas linhas de pesquisa na tese: 1) uma representação baseada em grafos associados a objetos multi-modais; e 2) uma abordagem baseada em aprendizado para identificar correspondências entre grafos. Inicialmente, nós investigamos o uso do método Sacola de Grafos Visuais para representar regiões na classificação de imagens de sensoriamento remoto, considerando a distribuição espacial de pontos de interesse dentro da imagem. Quando é feita a combinação de representações de cores e textura, nós obtivemos resultados efetivos em duas bases de dados da literatura (Monte Santo e Campinas). Em segundo lugar, nós propomos duas novas extensões do método de Sacola de Grafos para a representação de objetos multi-modais. Ao utilizar essas abordagens, nós combinamos visões complementares de diferentes modalidades (por exemplo, descrições visuais e textuais). Nós validamos o uso dessas abordagens no problema de detecção de enchentes proposto pela iniciativa MediaEval, obtendo 86,9\% de acurácia nos 50 primeiros resultados retornados. Nós abordamos o problema de corresponência de grafos ao propor um arcabouço original para aprender a função de custo no método de distância de edição de grafos. Nós também apresentamos algumas implementações utilizando métodos de reconhecimento em cenário aberto e medidas de redes complexas para caracterizar propriedades locais de grafos. Até onde sabemos, nós fomos os primeiros a tratar o processo de aprendizado de custo como um problema de reconhecimento em cenário aberto e os primeiros a explorar medidas de redes complexas em tais problemas. Nós obtivemos resultados efetivos, que são comparáveis a diversos métodos da literatura em problemas de classificação de grafosAbstract: Many real-world situations can be modeled through objects and their relationships, like the roads connecting cities in a map. Graph is a concept derived from the abstraction of these situations. Graphs are a powerful structural representation, which encodes relationship among objects and among their components into a single formalism. This representation is so powerful that it is applied to a wide range of applications, ranging from bioinformatics to social networks. Thus, several pattern recognition problems are modeled to use graph-based representations. In classification problems, the relationships among objects or among their components are exploited to achieve effective and/or efficient solutions. In this thesis, we investigate the use of graphs in classification problems. Two research venues are followed: 1) proposal of graph-based multimodal object representations; and 2) proposal of learning-based approaches to support graph matching. Firstly, we investigated the use of the recently proposed Bag-of-Visual-Graphs method in the representation of regions in a remote sensing classification problem, considering the spatial distribution of interest points within the image. When we combined color and texture representations, we obtained effective results in two datasets of the literature (Monte Santo and Campinas). Secondly, we proposed two new extensions of the Bag-of-Graphs method to the representation of multimodal objects. By using these approaches, we can combine complementary views of different modalities (e.g., visual and textual descriptions). We validated the use of these approaches in the flooding detection problem proposed by the MediaEval initiative, achieving 86.9\% of accuracy at the Precision@50. We addressed the graph matching problem by proposing an original framework to learn the cost function in a graph edit distance method. We also presented a couple of formulations using open-set recognition methods and complex network measurements to characterize local graph properties. To the best of our knowledge, we were the first to conduct the cost learning process as an open-set recognition problem and to exploit complex network measurements in such problems. We have achieved effective results, which are comparable to several baselines in graph classification problemsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2016/18429-141584/2016-5CAPESFAPESPCNP

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

Stereo Matching Using a Modified Efficient Belief Propagation in a Level Set Framework

Stereo matching determines correspondence between pixels in two or more images of the same scene taken from different angles; this can be handled either locally or globally. The two most common global approaches are belief propagation (BP) and graph cuts. Efficient belief propagation (EBP), which is the most widely used BP approach, uses a multi-scale message passing strategy, an O(k) smoothness cost algorithm, and a bipartite message passing strategy to speed up the convergence of the standard BP approach. As in standard belief propagation, every pixel sends messages to and receives messages from its four neighboring pixels in EBP. Each outgoing message is the sum of the data cost, incoming messages from all the neighbors except the intended receiver, and the smoothness cost. Upon convergence, the location of the minimum of the final belief vector is defined as the current pixel’s disparity. The present effort makes three main contributions: (a) it incorporates level set concepts, (b) it develops a modified data cost to encourage matching of intervals, (c) it adjusts the location of the minimum of outgoing messages for select pixels that is consistent with the level set method. When comparing the results of the current work with that of standard EBP, the disparity results are very similar, as they should be

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Discrete Optimization Methods for Segmentation and Matching

This dissertation studies discrete optimization methods for several computer vision problems. In the first part, a new objective function for superpixel segmentation is proposed. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. I present a new graph construction for images and show that this construction induces a matroid. The segmentation is then given by the graph topology which maximizes the objective function under the matroid constraint. By exploiting submodular and monotonic properties of the objective function, I develop an efficient algorithm with a worst-case performance bound of $\frac{1}{2}$ for the superpixel segmentation problem. Extensive experiments on the Berkeley segmentation benchmark show the proposed algorithm outperforms the state of the art in all the standard evaluation metrics. Next, I propose a video segmentation algorithm by maximizing a submodular objective function subject to a matroid constraint. This function is similar to the standard energy function in computer vision with unary terms, pairwise terms from the Potts model, and a novel higher-order term based on appearance histograms. I show that the standard Potts model prior, which becomes non-submodular for multi-label problems, still induces a submodular function in a maximization framework. A new higher-order prior further enforces consistency in the appearance histograms both spatially and temporally across the video. The matroid constraint leads to a simple algorithm with a performance bound of $\frac{1}{2}$. A branch and bound procedure is also presented to improve the solution computed by the algorithm. The last part of the dissertation studies the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem, chamfer matching remains to be the preferred method when speed and robustness are considered. In this dissertation, I significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). It is achieved by incorporating edge orientation information in the matching algorithm so the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, I present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows one to bound the cost distribution of large neighborhoods and skip the bad hypotheses. Experiments show that the proposed approach improves the speed of the original chamfer matching up to an order of 45 times, and it is much faster than many state of art techniques while the accuracy is comparable. I further demonstrate the application of the proposed algorithm in providing seamless operation for a robotic bin picking system

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

Multi-Object Shape Retrieval Using Curvature Trees

This work presents a geometry-based image retrieval approach for multi-object images. We commence with developing an effective shape matching method for closed boundaries. Then, a structured representation, called curvature tree (CT), is introduced to extend the shape matching approach to handle images containing multiple objects with possible holes. We also propose an algorithm, based on Gestalt principles, to detect and extract high-level boundaries (or envelopes), which may evolve as a result of the spatial arrangement of a group of image objects. At first, a shape retrieval method using triangle-area representation (TAR) is presented for non-rigid shapes with closed boundaries. This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, scaling and shear, and robust against noise and moderate amounts of occlusion. For matching, two algorithms are introduced. The first algorithm matches concavity maxima points extracted from TAR image obtained by thresholding the TAR. In the second matching algorithm, dynamic space warping (DSW) is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Experimental results using the MPEG-7 CE-1 database of 1400 shapes show the superiority of our method over other recent methods. Then, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT). To facilitate shape-based matching, the TAR of each object and hole is stored at the corresponding node in the CT. The similarity between two CTs is measured based on the maximum similarity subtree isomorphism (MSSI) where a one-to-one correspondence is established between the nodes of the two trees. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Two algorithms are introduced to solve the MSSI problem: an approximate and an exact. Both algorithms have polynomial-time computational complexity and use the DSW as the similarity measure between the attributed nodes. Experiments on a database of 13500 medical images and a database of 1580 logo images have shown the effectiveness of the proposed method. The purpose of the last part is to allow for high-level shape retrieval in multi-object images by detecting and extracting the envelope of high-level object groupings in the image. Motivated by studies in Gestalt theory, a new algorithm for the envelope extraction is proposed that works in two stages. The first stage detects the envelope (if exists) and groups its objects using hierarchical clustering. In the second stage, each grouping is merged using morphological operations and then further refined using concavity tree reconstruction to eliminate odd concavities in the extracted envelope. Experiment on a set of 110 logo images demonstrates the feasibility of our approach