On Randomly Projected Hierarchical Clustering with Guarantees

Hierarchical clustering (HC) algorithms are generally limited to small data instances due to their runtime costs. Here we mitigate this shortcoming and explore fast HC algorithms based on random projections for single (SLC) and average (ALC) linkage clustering as well as for the minimum spanning tree problem (MST). We present a thorough adaptive analysis of our algorithms that improve prior work from $O(N^2)$ by up to a factor of $N/(\log N)^2$ for a dataset of $N$ points in Euclidean space. The algorithms maintain, with arbitrary high probability, the outcome of hierarchical clustering as well as the worst-case running-time guarantees. We also present parameter-free instances of our algorithms.Comment: This version contains the conference paper "On Randomly Projected Hierarchical Clustering with Guarantees'', SIAM International Conference on Data Mining (SDM), 2014 and, additionally, proofs omitted in the conference versio

Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

Abstract. Approaches to object localization based on codebooks do not exploit the dependencies between appearance and geometric information present in training data. This work addresses the problem of computing a codebook tailored to the task of localization by applying regularization based on geometric information. We present a novel method, the Regularized Combined Partitional-Agglomerative clustering, which extends the standard CPA method by adding extra knowledge to the clustering process to preserve as much geometric information as needed. Due to the time complexity of the methodology, we also present an implementation on the GPU using nVIDIA CUDA technology, speeding up the process with a factor over 100x

Fast Neighbor Search By Using Revised K-D Tree

We present two new neighbor query algorithms, including range query (RNN) and nearest neighbor (NN) query, based on revised k-d tree by using two techniques. The first technique is proposed for decreasing unnecessary distance computations by checking whether the cell of a node is inside or outside the specified neighborhood of query point, and the other is used to reduce redundant visiting nodes by saving the indices of descendant points. We also implement the proposed algorithms in Matlab and C. The Matlab version is to improve original RNN and NN which are based on k-d tree, C version is to improve k-Nearest neighbor query (kNN) which is based on buffer k-d tree. Theoretical and experimental analysis have shown that the proposed algorithms significantly improve the original RNN, NN and kNN in low dimension, respectively. The tradeoff is that the additional space cost of the revised k-d tree is approximately O(αnlog (n))

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications

대용량 데이터 탐색을 위한 점진적 시각화 시스템 설계

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 서진욱.Understanding data through interactive visualization, also known as visual analytics, is a common and necessary practice in modern data science. However, as data sizes have increased at unprecedented rates, the computation latency of visualization systems becomes a significant hurdle to visual analytics. The goal of this dissertation is to design a series of systems for progressive visual analytics (PVA)—a visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these results—to address the scalability hurdle. To support the interactive exploration of data with billions of records, we first introduce SwiftTuna, an interactive visualization system with scalable visualization and computation components. Our performance benchmark demonstrates that it can handle data with four billion records, giving responsive feedback every few seconds without precomputation. Second, we present PANENE, a progressive algorithm for the Approximate k-Nearest Neighbor (AKNN) problem. PANENE brings useful machine learning methods into visual analytics, which has been challenging due to their long initial latency resulting from AKNN computation. In particular, we accelerate t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular non-linear dimensionality reduction technique, which enables the responsive visualization of data with a few hundred columns. Each of these two contributions aims to address the scalability issues stemming from a large number of rows or columns in data, respectively. Third, from the users' perspective, we focus on improving the trustworthiness of intermediate knowledge gained from uncertain results in PVA. We propose a novel PVA concept, Progressive Visual Analytics with Safeguards, and introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified. We also present a proof-of-concept system, ProReveal, designed and developed to integrate seven safeguards into progressive data exploration. Our user study demonstrates that people not only successfully created PVA-Guards on ProReveal but also voluntarily used PVA-Guards to manage the uncertainty of their knowledge. Finally, summarizing the three studies, we discuss design challenges for progressive systems as well as future research agendas for PVA.현대 데이터 사이언스에서 인터랙티브한 시각화를 통해 데이터를 이해하는 것은 필수적인 분석 방법 중 하나이다. 그러나, 최근 데이터의 크기가 폭발적으로 증가하면서 데이터 크기로 인해 발생하는 지연 시간이 인터랙티브한 시각적 분석에 큰 걸림돌이 되었다. 본 연구에서는 이러한 확장성 문제를 해결하기 위해 점진적 시각적 분석(Progressive Visual Analytics)을 지원하는 일련의 시스템을 디자인하고 개발한다. 이러한 점진적 시각적 분석 시스템은 데이터 처리가 완전히 끝나지 않더라도 중간 분석 결과를 사용자에게 제공함으로써 데이터의 크기로 인해 발생하는 지연 시간 문제를 완화할 수 있다. 첫째로, 수십억 건의 행을 가지는 데이터를 시각적으로 탐색할 수 있는 SwiftTuna 시스템을 제안한다. 데이터 처리 및 시각적 표현의 확장성을 목표로 개발된 이 시스템은, 약 40억 건의 행을 가진 데이터에 대한 시각화를 전처리 없이 수 초마다 업데이트할 수 있는 것으로 나타났다. 둘째로, 근사적 k-최근접점(Approximate k-Nearest Neighbor) 문제를 점진적으로 계산하는 PANENE 알고리즘을 제안한다. 근사적 k-최근접점 문제는 여러 기계 학습 기법에서 쓰임에도 불구하고 초기 계산 시간이 길어서 인터랙티브한 시스템에 적용하기 힘든 한계가 있었다. PANENE 알고리즘은 이러한 긴 초기 계산 시간을 획기적으로 개선하여 다양한 기계 학습 기법을 시각적 분석에 활용할 수 있도록 한다. 특히, 유용한 비선형적 차원 감소 기법인 t-분포 확률적 임베딩(t-Distributed Stochastic Neighbor Embedding)을 가속하여 수백 개의 차원을 가지는 데이터를 빠른 시간 내에 사영할 수 있다. 위의 두 시스템과 알고리즘이 데이터의 행 또는 열의 개수로 인한 확장성 문제를 해결하고자 했다면, 세 번째 시스템에서는 점진적 시각적 분석의 신뢰도 문제를 개선하고자 한다. 점진적 시각적 분석에서 사용자에게 주어지는 중간 계산 결과는 최종 결과의 근사치이므로 불확실성이 존재한다. 본 연구에서는 세이프가드를 이용한 점진적 시각적 분석(Progressive Visual Analytics with Safeguards)이라는 새로운 개념을 제안한다. 이 개념은 사용자가 점진적 탐색에서 마주하는 불확실한 중간 지식에 세이프가드를 남길 수 있도록 하여 탐색에서 얻은 지식의 정확도를 추후 검증할 수 있도록 한다. 또한, 이러한 개념을 실제로 구현하여 탑재한 ProReveal 시스템을 소개한다. ProReveal를 이용한 사용자 실험에서 사용자들은 세이프가드를 성공적으로 만들 수 있었을 뿐만 아니라, 중간 지식의 불확실성을 다루기 위해 세이프가드를 자발적으로 이용한다는 것을 알 수 있었다. 마지막으로, 위 세 가지 연구의 결과를 종합하여 점진적 시각적 분석 시스템을 구현할 때의 디자인적 난제와 향후 연구 방향을 모색한다.CHAPTER1. Introduction 2 1.1 Background and Motivation 2 1.2 Thesis Statement and Research Questions 5 1.3 Thesis Contributions 5 1.3.1 Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 6 1.3.2 ProgressiveComputation of Approximate k-Nearest Neighbors and Responsive t-SNE 7 1.3.3 Progressive Visual Analytics with Safeguards 8 1.4 Structure of Dissertation 9 CHAPTER2. Related Work 11 2.1 Progressive Visual Analytics 11 2.1.1 Definitions 11 2.1.2 System Latency and Human Factors 13 2.1.3 Users, Tasks, and Models 15 2.1.4 Techniques, Algorithms, and Systems. 17 2.1.5 Uncertainty Visualization 19 2.2 Approaches for Scalable Visualization Systems 20 2.3 The k-Nearest Neighbor (KNN) Problem 22 2.4 t-Distributed Stochastic Neighbor Embedding 26 CHAPTER3. SwiTuna: Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 28 3.1 The SwiTuna Design 31 3.1.1 Design Considerations 32 3.1.2 System Overview 33 3.1.3 Scalable Visualization Components 36 3.1.4 Visualization Cards 40 3.1.5 User Interface and Interaction 42 3.2 Responsive Querying 44 3.2.1 Querying Pipeline 44 3.2.2 Prompt Responses 47 3.2.3 Incremental Processing 47 3.3 Evaluation: Performance Benchmark 49 3.3.1 Study Design 49 3.3.2 Results and Discussion 52 3.4 Implementation 56 3.5 Summary 56 CHAPTER4. PANENE:AProgressive Algorithm for IndexingandQuerying Approximate k-Nearest Neighbors 58 4.1 Approximate k-Nearest Neighbor 61 4.1.1 A Sequential Algorithm 62 4.1.2 An Online Algorithm 63 4.1.3 A Progressive Algorithm 66 4.1.4 Filtered AKNN Search 71 4.2 k-Nearest Neighbor Lookup Table 72 4.3 Benchmark. 78 4.3.1 Online and Progressive k-d Trees 78 4.3.2 k-Nearest Neighbor Lookup Tables 83 4.4 Applications 85 4.4.1 Progressive Regression and Density Estimation 85 4.4.2 Responsive t-SNE 87 4.5 Implementation 92 4.6 Discussion 92 4.7 Summary 93 CHAPTER5. ProReveal: Progressive Visual Analytics with Safeguards 95 5.1 Progressive Visual Analytics with Safeguards 98 5.1.1 Definition 98 5.1.2 Examples 101 5.1.3 Design Considerations 103 5.2 ProReveal 105 5.3 Evaluation 121 5.4 Discussion 127 5.5 Summary 130 CHAPTER6. Discussion 132 6.1 Lessons Learned 132 6.2 Limitations 135 CHAPTER7. Conclusion 137 7.1 Thesis Contributions Revisited 137 7.2 Future Research Agenda 139 7.3 Final Remarks 141 Abstract (Korean) 155 Acknowledgments (Korean) 157Docto

Collaborative Appearance-Based Place Recognition and Improving Place Recognition Using Detection of Dynamic Objects

This dissertation makes contributions to the problem of Long-Term Appearance-Based Place Recognition. We present a framework for place recognition in a collaborative scheme and a method to reduce the impact of dynamic objects on place representations. We demonstrate our findings using a state-of-the-art place recognition approach. We begin in Part I by describing the general problem of place recognition and its importance in applications where accurate localization is crucial. We discuss feature detection and description and also explain the functioning of several place recognition frameworks. In Part II, we present a novel framework for collaboration between agents from a pure appearance-based place recognition perspective. Using this framework, multiple agents can efficiently share partial or complete knowledge about places and benefit from their teamwork. This collaborative framework allows agents with limited storage and memory capacity to become useful in environment exploration tasks (for instance, by enabling remote recognition); includes procedures to manage an agent’s memory load and distributes knowledge of places across agents; allows the reuse of knowledge from one agent to another; and increases the tolerance for failure of individual agents. Part II also defines metrics which allow us to measure the performance of a system that uses the collaborative framework. Finally, in Part III, we present an innovative method to improve the recognition of places in environments densely populated by dynamic objects. We demonstrate that we can improve the recognition performance in these environments by incorporating high- level information from dynamic objects. Tests conducted using a synthetic dataset show the benefits of our approach. The proposed method allows the system to significantly improve the recognition performance in the photo-realistic dataset while reducing storage requirements, resulting in up to 23.7 percent less storage space than the state-of-the-art approach that we have extended; smaller representations also reduced the time required to match places. In Part III, we also formulate the concept of a valid place representation and determine the quality of the observation based on dynamic objects present in the agent’s view. Of course, recognition systems that are sensitive to dynamic objects incur additional computational costs to recognize those objects. We show that this additional cost is outweighed by the benefits that incorporating dynamic object detection in the place recognition pipeline. Our findings can be used in many applications, including applications for navigation, e.g. assisting visually impaired individuals with navigating indoors, or autonomous vehicles

Visual vocabularies for category-level object recognition

This thesis focuses on the study of visual vocabularies for category-level object recognition. Specifically, we state novel approaches for building visual codebooks. Our aim is not just to obtain more discriminative and more compact visual codebooks, but to bridge the gap between visual features and semantic concepts. A novel approach for obtaining class representative visual words is presented. It is based on a maximisation procedure, i. e. the Cluster Precision Maximisation (CPM), of a novel cluster precision criterion, and on an adaptive threshold refinement scheme for agglomerative clustering algorithms based on correlation clustering techniques. The objective is to increase the vocabulary compactness while at the same time improve the recognition rate and further increase the representativeness of the visual words. Moreover, we describe a novel clustering aggregation based approach for building efficient and semantic visual vocabularies. It consist of a novel framework for incorporating neighboring appearances of local descriptors into the vocabulary construction, and a rigorous approach for adding meaningful spatial coherency among the local features into the visual codebooks. We also propose an efficient high-dimensional data clustering algorithm, the Fast Reciprocal Nearest Neighbours (Fast-RNN). Our approach, which is a speeded up version of the standard RNN algorithm, is based on the projection search paradigm. Finally, we release a new database of images called Image Collection of Annotated Real-world Objects (ICARO), which is especially designed for evaluating category-level object recognition systems. An exhaustive comparison of ICARO with other well-known datasets used within the same context is carried out. We also propose a benchmark for both object classification and detection

Visual vocabularies for category-level object recognition

This thesis focuses on the study of visual vocabularies for category-level object recognition. Specifically, we state novel approaches for building visual codebooks. Our aim is not just to obtain more discriminative and more compact visual codebooks, but to bridge the gap between visual features and semantic concepts. A novel approach for obtaining class representative visual words is presented. It is based on a maximisation procedure, i. e. the Cluster Precision Maximisation (CPM), of a novel cluster precision criterion, and on an adaptive threshold refinement scheme for agglomerative clustering algorithms based on correlation clustering techniques. The objective is to increase the vocabulary compactness while at the same time improve the recognition rate and further increase the representativeness of the visual words. Moreover, we describe a novel clustering aggregation based approach for building efficient and semantic visual vocabularies. It consist of a novel framework for incorporating neighboring appearances of local descriptors into the vocabulary construction, and a rigorous approach for adding meaningful spatial coherency among the local features into the visual codebooks. We also propose an efficient high-dimensional data clustering algorithm, the Fast Reciprocal Nearest Neighbours (Fast-RNN). Our approach, which is a speeded up version of the standard RNN algorithm, is based on the projection search paradigm. Finally, we release a new database of images called Image Collection of Annotated Real-world Objects (ICARO), which is especially designed for evaluating category-level object recognition systems. An exhaustive comparison of ICARO with other well-known datasets used within the same context is carried out. We also propose a benchmark for both object classification and detection