Recent Advance in Content-based Image Retrieval: A Literature Survey

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.Comment: 22 page

Large Scale Audio-Visual Video Analytics Platform for Forensic Investigations of Terroristic Attacks

The forensic investigation of a terrorist attack poses a huge challenge to the investigative authorities, as several thousand hours of video footage need to be spotted. To assist law enforcement agencies (LEA) in identifying suspects and securing evidences, we present a platform which fuses information of surveillance cameras and video uploads from eyewitnesses. The platform integrates analytical modules for different input-modalities on a scalable architecture. Videos are analyzed according their acoustic and visual content. Specifically, Audio Event Detection is applied to index the content according to attack-specific acoustic concepts. Audio similarity search is utilized to identify similar video sequences recorded from different perspectives. Visual object detection and tracking are used to index the content according to relevant concepts. The heterogeneous results of the analytical modules are fused into a distributed index of visual and acoustic concepts to facilitate rapid start of investigations, following traits and investigating witness reports

Scalable Similarity Joins of Tokenized Strings

This work tackles the problem of fuzzy joining of strings that naturally tokenize into meaningful substrings, e.g., full names. Tokenized-string joins have several established applications in the context of data integration and cleaning. This work is primarily motivated by fraud detection, where attackers slightly modify tokenized strings, e.g., names on accounts, to create numerous identities that she can use to defraud service providers, e.g., Google, and LinkedIn. To detect such attacks, all the accounts are pair-wise compared, and the resulting similar accounts are considered suspicious and are further investigated. Comparing the tokenized-string features of a large number of accounts requires an intuitive tokenized-string distance that can detect subtle edits introduced by an adversary, and a very scalable algorithm. This is not achievable by existing distance measure that are unintuitive, hard to tune, and whose join algorithms are serial and hence unscalable. We define a novel intuitive distance measure between tokenized strings, Normalized Setwise Levenshtein Distance (NSLD). To the best of our knowledge, NSLD is the first metric proposed for comparing tokenized strings. We propose a scalable distributed framework, Tokenized-String Joiner (TSJ), that adopts existing scalable string-join algorithms as building blocks to perform NSLD-joins. We carefully engineer optimizations and approximations that dramatically improve the efficiency of TSJ. The effectiveness of the TSJ framework is evident from the evaluation conducted on tens of millions of tokenized-string names from Google accounts. The superiority of the tokenized-string-specific TSJ framework over the general-purpose metric-spaces joining algorithms has been established

Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval

Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency. However, the learned hash codes are embedded with limited discriminative semantics due to the intrinsic limitation of image representation. To address the problem, in this paper, we propose a novel hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH). The key idea is to \emph{directly} augment the semantics of discrete image hash codes by exploring auxiliary contextual modalities. To this end, a unified hashing framework is formulated to simultaneously preserve visual similarities of images and perform semantic transfer from contextual modalities. Further, to guarantee direct semantic transfer and avoid information loss, we explicitly impose the discrete constraint, bit--uncorrelation constraint and bit-balance constraint on hash codes. A novel and effective discrete optimization method based on augmented Lagrangian multiplier is developed to iteratively solve the optimization problem. The whole learning process has linear computation complexity and desirable scalability. Experiments on three benchmark datasets demonstrate the superiority of DSTH compared with several state-of-the-art approaches

PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems

In the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks particularly demonstrate this limitation when their data is represented using few random variables while each random variable has a massive set of values. With hierarchical data - data that is arranged in a treelike structure with several levels - one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node. In this paper we present a scalable probabilistic graphical model to overcome these limitations for massive hierarchical data. We believe the proposed model will lead to an easily-scalable, more readable, and expressive implementation for problems that require probabilistic-based solutions for massive amounts of hierarchical data. We successfully applied this model to solve two different challenging probabilistic-based problems on massive hierarchical data sets for different domains, namely, bioinformatics and latent semantic discovery over search logs

Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs

We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures, which are typically used at the coarse search stage of the most proximity graph techniques. Hierarchical NSW incrementally builds a multi-layer structure consisting from hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.Comment: 13 pages, 15 figure

Queries mining for efficient routing in P2P communities

Peer-to-peer (P2P) computing is currently attracting enormous attention. In P2P systems a very large number of autonomous computing nodes (the peers) pool together their resources and rely on each other for data and services. Peer-to-peer (P2P) Data-sharing systems now generate a significant portion of Internet traffic. Examples include P2P systems for network storage, web caching, searching and indexing of relevant documents and distributed network-threat analysis. Requirements for widely distributed information systems supporting virtual organizations have given rise to a new category of P2P systems called schema-based. In such systems each peer exposes its own schema and the main objective is the efficient search across the P2P network by processing each incoming query without overly consuming bandwidth. The usability of these systems depends on effective techniques to find and retrieve data; however, efficient and effective routing of content-based queries is a challenging problem in P2P networks. This work was attended as an attempt to motivate the use of mining algorithms and hypergraphs context to develop two different methods that improve significantly the efficiency of P2P communications. The proposed query routing methods direct the query to a set of relevant peers in such way as to avoid network traffic and bandwidth consumption. We compare the performance of the two proposed methods with the baseline one and our experimental results prove that our proposed methods generate impressive levels of performance and scalability.Comment: 20 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:1108.137

Open Source Face Recognition Performance Evaluation Package

Biometrics-related research has been accelerated significantly by deep learning technology. However, there are limited open-source resources to help researchers evaluate their deep learning-based biometrics algorithms efficiently, especially for the face recognition tasks. In this work, we design and implement a light-weight, maintainable, scalable, generalizable, and extendable face recognition evaluation toolbox named FaRE that supports both online and offline evaluation to provide feedback to algorithm development and accelerate biometrics-related research. FaRE consists of a set of evaluation metric functions and provides various APIs for commonly-used face recognition datasets including LFW, CFP, UHDB31, and IJB-series datasets, which can be easily extended to include other customized datasets. The package and the pre-trained baseline models will be released for public academic research use after obtaining university approval.Comment: Technical repor

An interdisciplinary survey of network similarity methods

Comparative graph and network analysis play an important role in both systems biology and pattern recognition, but existing surveys on the topic have historically ignored or underserved one or the other of these fields. We present an integrative introduction to the key objectives and methods of graph and network comparison in each field, with the intent of remaining accessible to relative novices in order to mitigate the barrier to interdisciplinary idea crossover. To guide our investigation, and to quantitatively justify our assertions about what the key objectives and methods of each field are, we have constructed a citation network containing 5,793 vertices from the full reference lists of over two hundred relevant papers, which we collected by searching Google Scholar for ten different network comparison-related search terms. We investigate its basic statistics and community structure, and frame our presentation around the papers found to have high importance according to five different standard centrality measures

Scalable Object Detection for Stylized Objects

Following recent breakthroughs in convolutional neural networks and monolithic model architectures, state-of-the-art object detection models can reliably and accurately scale into the realm of up to thousands of classes. Things quickly break down, however, when scaling into the tens of thousands, or, eventually, to millions or billions of unique objects. Further, bounding box-trained end-to-end models require extensive training data. Even though - with some tricks using hierarchies - one can sometimes scale up to thousands of classes, the labor requirements for clean image annotations quickly get out of control. In this paper, we present a two-layer object detection method for brand logos and other stylized objects for which prototypical images exist. It can scale to large numbers of unique classes. Our first layer is a CNN from the Single Shot Multibox Detector family of models that learns to propose regions where some stylized object is likely to appear. The contents of a proposed bounding box is then run against an image index that is targeted for the retrieval task at hand. The proposed architecture scales to a large number of object classes, allows to continously add new classes without retraining, and exhibits state-of-the-art quality on a stylized object detection task such as logo recognition.Comment: 9 pages, 7 figure