Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space

FROM RAW DATA TO PROCESSABLE INFORMATIVE DATA: TRAINING DATA MANAGEMENT FOR BIG DATA ANALYTICS

Ph.DDOCTOR OF PHILOSOPH

A Novel Accuracy and Similarity Search Structure Based on Parallel Bloom Filters

In high-dimensional spaces, accuracy and similarity search by low computing and storage costs are always difficult research topics, and there is a balance between efficiency and accuracy. In this paper, we propose a new structure Similar-PBF-PHT to represent items of a set with high dimensions and retrieve accurate and similar items. The Similar-PBF-PHT contains three parts: parallel bloom filters (PBFs), parallel hash tables (PHTs), and a bitmatrix. Experiments show that the Similar-PBF-PHT is effective in membership query and K-nearest neighbors (K-NN) search. With accurate querying, the Similar-PBF-PHT owns low hit false positive probability (FPP) and acceptable memory costs. With K-NN querying, the average overall ratio and rank-i ratio of the Hamming distance are accurate and ratios of the Euclidean distance are acceptable. It takes CPU time not I/O times to retrieve accurate and similar items and can deal with different data formats not only numerical values

Kernelized Locality-Sensitive Hashing for Fast Image Landmark Association

As the concept of war has evolved, navigation in urban environments where GPS may be degraded is increasingly becoming more important. Two existing solutions are vision-aided navigation and vision-based Simultaneous Localization and Mapping (SLAM). The problem, however, is that vision-based navigation techniques can require excessive amounts of memory and increased computational complexity resulting in a decrease in speed. This research focuses on techniques to improve such issues by speeding up and optimizing the data association process in vision-based SLAM. Specifically, this work studies the current methods that algorithms use to associate a current robot pose to that of one previously seen and introduce another method to the image mapping arena for comparison. The current method, kd-trees, is effcient in lower dimensions, but does not narrow the search space enough in higher dimensional datasets. In this research, Kernelized Locality-Sensitive Hashing (KLSH) is implemented to conduct the aforementioned pose associations. Results on KLSH shows that fewer image comparisons are required for location identification than that of other methods. This work can then be extended into a vision-SLAM implementation to subsequently produce a map

Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

A Survey of Visual Transformers

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern Convolution Neural Networks (CNNs). In this survey, we have reviewed over one hundred of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where a taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, three promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at https://github.com/liuyang-ict/awesome-visual-transformers.Comment: Update the applications of both 3D point clouds and multi-sensory data strea

A framework for digitisation of manual manufacturing task knowledge using gaming interface technology

Intense market competition and the global skill supply crunch are hurting the manufacturing industry, which is heavily dependent on skilled labour. Companies must look for innovative ways to acquire manufacturing skills from their experts and transfer them to novices and eventually to machines to remain competitive. There is a lack of systematic processes in the manufacturing industry and research for cost-effective capture and transfer of human skills. Therefore, the aim of this research is to develop a framework for digitisation of manual manufacturing task knowledge, a major constituent of which is human skill. The proposed digitisation framework is based on the theory of human-workpiece interactions that is developed in this research. The unique aspect of the framework is the use of consumer-grade gaming interface technology to capture and record manual manufacturing tasks in digital form to enable the extraction, decoding and transfer of manufacturing knowledge constituents that are associated with the task. The framework is implemented, tested and refined using 5 case studies, including 1 toy assembly task, 2 real-life-like assembly tasks, 1 simulated assembly task and 1 real-life composite layup task. It is successfully validated based on the outcomes of the case studies and a benchmarking exercise that was conducted to evaluate its performance. This research contributes to knowledge in five main areas, namely, (1) the theory of human-workpiece interactions to decipher human behaviour in manual manufacturing tasks, (2) a cohesive and holistic framework to digitise manual manufacturing task knowledge, especially tacit knowledge such as human action and reaction skills, (3) the use of low-cost gaming interface technology to capture human actions and the effect of those actions on workpieces during a manufacturing task, (4) a new way to use hidden Markov modelling to produce digital skill models to represent human ability to perform complex tasks and (5) extraction and decoding of manufacturing knowledge constituents from the digital skill models

Contribuições para a localização e mapeamento em robótica através da identificação visual de lugares

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015Em robótica móvel, os métodos baseados na aparência visual constituem umaabordagem atractiva para o tratamento dos problemas da localização e mapeamento.Contudo, para o seu sucesso é fundamental o uso de características visuais suficientemente discriminativas. Esta é uma condição necessária para assegurar o reconhecimento de lugares na presença de factores inibidores, tais como a semelhança entre lugares ou as variações de luminosidade. Esta tese debruça-se sobre os problemas de localização e mapeamento, tendo como objectivo transversal a obtenção de representações mais discriminativas ou com menores custos computacionais. Em termos gerais, dois tipos de características visuais são usadas, as características locais e globais. A aplicação de características locais na descrição da aparência tem sido dominada pelo modelo BoW (Bag-of-Words), segundo o qual os descritores são quantizados e substituídos por palavras visuais. Nesta tese questiona-se esta opção através do estudo da abordagem alternativa, a representação não-quantizada (NQ). Em resultado deste estudo, contribui-se com um novo método para a localização global de robôs móveis,o classificador NQ. Este, para além de apresentar maior precisão do que o modeloBoW, admite simplificações importantes que o tornam competitivo, também emtermos de eficiência, com a representação quantizada. Nesta tese é também estudado o problema anterior à localização, o da extracção de um mapa do ambiente, sendo focada, em particular, a detecção da revisitação de lugares. Para o tratamento deste problema é proposta uma nova característica global,designada LBP-Gist, que combina a análise de texturas pelo método LBP com a codificação da estrutura global da imagem, inerente à característica Gist. A avaliação deste método em vários datasets demonstra a viabilidade do detector proposto, o qual apresenta precisão e eficiência superiores ao state-of–the-art em ambientes de exterior.In the mobile robotics field, appearance-based methods are at the core of several attractive systems for localization and mapping. To be successful, however, these methods require features having good descriptive power. This is a necessary condition to ensure place recognition in the presence of disturbing factors, such as high similarity between places or lighting variations. This thesis addresses the localization and mapping problems, globally seeking representations which are more discriminative or more efficient. To this end, two broad types of visual features are used, local and global features. Appearance representations based on local features have been dominated by the BoW (Bag of Words) model, which prescribes the quantization of descriptors and their labelling with visual words. In this thesis, this method is challenged through the study of the alternative approach, the non-quantized representation (NQ). As an outcome of this study, we contribute with a novel global localization method, the NQ classifier. Besides offering higher precision than the BoW model, this classifier is susceptible of significant simplifications, through which it is made competitive to the quantized representation in terms of efficiency. This thesis also addresses the problem posed prior to localization, the mapping of the environment, focusing specifically on the loop closure detection task. To support loop closing, a new global feature, LBP-Gist, is proposed. As the name suggests, this feature combines texture analysis, provided by the LBP method, with the encoding of global image structure, underlying the Gist feature. Evaluation on several datasets demonstrates the validity of the proposed detector. Concretely, precision and efficiency of the method are shown to be superior to the state-of-the-art in outdoor environments