Heng Qi

An effective solution for trademark image retrieval by combining shape description and feature matching

Trademark image retrieval (TIR), a branch of content-based image retrieval (CBIR), is playing an important role in multimedia information retrieval. This paper proposes an effective solution for TIR by combining shape description and feature matching. We first present an effective shape description method which includes two shape descriptors. Second, we propose an effective feature matching strategy to compute the dissimilarity value between the feature vectors extracted from images. Finally, we combine the shape description method and the feature matching strategy to realize our solution. We conduct a large number of experiments on a standard image set to evaluate our solution and the existing solutions. By comparison of their experimental results, we can see that the proposed solution outperforms existing solutions for the widely used performance metrics.

Detecting DDoS attacks against data center with correlation analysis

Distributed denial-of-service (DDoS) attacks pose a great threat to the data center, and many defense mechanisms have been proposed to detect it. On one hand, many services deployed in data center can easily lead to corresponding DDoS attacks. On the other hand, attackers constantly modify their tools to bypass these existing mechanisms, and researchers in turn modify their approaches to handle new attacks. Thus, the DDoS against data center is becoming more and more complex. In this paper, we first analyze the correlation information of flows in data center. Second, we present an effective detection approach based on CKNN (K-nearest neighbors traffic classification with correlation analysis) to detect DDoS attacks. The approach exploits correlation information of training data to improve the classification accuracy and reduce the overhead caused by the density of training data. Aiming at solving the huge cost, we also present a grid-based method named r-polling method for reducing training data involved in the calculation. Finally, we evaluate our approach with the Internet traffic and data center traffic trace. Compared with the traditional methods, our approach is good at detecting abnormal traffic with high efficiency, low cost and wide detection range.

An effective discretization method for disposing high-dimensional data

Abstract Feature discretization is an extremely important preprocessing task used for classification in data mining and machine learning as many classification methods require that each dimension of the training dataset contains only discrete values. Most of discretization methods mainly concentrate on discretizing low-dimensional data. In this paper, we focus on discretizing high-dimensional data that frequently present the nonlinear structures. Firstly, we present a novel supervised dimension reduction algorithm to map high-dimensional data into a low-dimensional space, which ensures to keep intrinsic correlation structure of the original data. This algorithm overcomes the deficiency that the geometric topology of the data is easily distorted when mapping data that present an uneven distribution in high-dimensional space. To the best of our knowledge, this is the first approach to solve high-dimensional nonlinear data discretization with a dimension reduction technique. Secondly, we propose a supervised area-based chi-square discretization algorithm to effectively discretize each continuous dimension in the low-dimensional space. This algorithm overcomes the deficiency that existing methods do not consider the possibility of being merged for each interval pair from the view of probability. Finally, we conduct the experiments to evaluate the performance of the proposed method. The results show that our method achieves higher classification accuracy and yields a more concise knowledge of the data especially for high-dimensional datasets than existing discretization methods. In addition, our discretization method has also been successfully applied to computer vision and image classification.

RFID cardinality estimation with blocker tags

The widely used RFID tags impose serious privacy concerns as a tag responds to queries from readers no matter they are authorized or not. The common solution is to use a commercially available blocker tag which behaves as if a set of tags with known blocking IDs are present. The use of blocker tags makes RFID estimation much more challenging as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. The basic idea of REB is to conduct statistically inference from the two sets of responses and estimate the number of genuine tags. We conduct extensive simulations to evaluate the performance of REB, in terms of time-efficiency and estimation reliability. The experimental results reveal that our REB scheme runs tens of times faster than the fastest identification protocol with the same accuracy requirement.

Sampling Bloom Filter-Based Detection of Unknown RFID Tags

Unknown RFID tags appear when the unread tagged objects are moved in or tagged objects are misplaced. This paper studies the practically important problem of unknown tag detection while taking both time-efficiency and energy-efficiency of battery-powered active tags into consideration. We first propose a Sampling Bloom Filter which generalizes the standard Bloom Filter. Using the new filtering technique, we propose the Sampling Bloom Filter-based Unknown tag Detection Protocol (SBF-UDP), whose detection accuracy is tunable by the end users. We present the theoretical analysis to minimize the time and energy costs. SBF-UDP can be tuned to either the time-saving mode or the energy-saving mode, according to the specific requirements. Extensive simulations are conducted to evaluate the performance of the proposed protocol. The experimental results show that SBF-UDP considerably outperforms the previous related protocols in terms of both time-efficiency and energy-efficiency. For example, when 3 or more unknown tags appear in the RFID system with 30000 known tags, the proposed SBF-UDP is able to successfully report the existence of unknown tags with a confidence more than 99%. While our protocol runs 9 times faster than the fastest existing scheme and reducing the energy consumption by more than 80%.

Fast Tracking the Population of Key Tags in Large-Scale Anonymous RFID Systems

In large-scale radio frequency identification (RFID)-enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost.

Object-based image retrieval with kernel on adjacency matrix and local combined features

In object-based image retrieval, there are two important issues: an effective image representation method for representing image content and an effective image classification method for processing user feedback to find more images containing the user-desired object categories. In the image representation method, the local-based representation is the best selection for object-based image retrieval. As a kernel-based classification method, Support Vector Machine (SVM) has shown impressive performance on image classification. But SVM cannot work on the local-based representation unless there is an appropriate kernel. To address this problem, some representative kernels are proposed in literatures. However, these kernels cannot work effectively in object-based image retrieval due to ignoring the spatial context and the combination of local features. In this article, we present Adjacent Matrix (AM) and the Local Combined Features (LCF) to incorporate the spatial context and the combination of local features into the kernel. We propose the AM-LCF feature vector to represent image content and the AM-LCF kernel to measure the similarities between AM-LCF feature vectors. According to the detailed analysis, we show that the proposed kernel can overcome the deficiencies of existing kernels. Moreover, we evaluate the proposed kernel through experiments of object-based image retrieval on two public image sets. The experimental results show that the performance of object-based image retrieval can be improved by the proposed kernel.

Zebra: An East-West Control Framework for SDN Controllers

Traditional networks are surprisingly fragile and difficult to manage. Software Defined Networking (SDN) gained significant attention from both academia and industry, as if simplify network management through centralized configuration. Existing work primarily focuses on networks of limited scope such as data-centers and enterprises, which makes the development of SDN hindered when it comes to large-scale network environments. One way of enabling communication between data-centers, enterprises and ISPs in a large-scale network is to establish a standard communication mechanism between these entities. In this paper, we propose Zebra, a framework for enabling communication between different SDN domains. Zebra has two modules: Heterogeneous Controller Management (HCM) module and Domain Relationships Management (DRM) module. HCM collects network information from a group of controllers with no interconnection and generate a domain-wide network view. DRM collects network information from other domains to generate a global-wide network view. Moreover, HCM supports different SDN controllers, such as floodlight, maestro and so on. To test this framework, we develop a prototype system, and give some experimental results.

Robust subspace segmentation via nonconvex low rank representation

A nonconvex formulation to determine the low rank representation from contaminated data is proposed.We provide a proximal iteratively reweighed algorithm for solving the nonconvex model.The proposed nonconvex model can recover the underlying low rank structure of subspaces in spite of noisy corruptions. Recently, low rank representation (LRR) has been successfully applied to explore subspace segmentation of data. In this paper, we propose a nonconvex formulation to determine the LRR from contaminated data. Unlike in traditional methods, which directly utilize the nuclear norm to approximate the rank function and penalize noise using the ?2,1-norm, our method introduces the Ky Fan p-k-norm and the ?2,q-norm, to better approximate the rank minimization problem and enhance the robustness against noise. An efficient algorithm is derived for solving the novel objective function, and this is followed by a rigorous theoretical proof of the convergence. Extensive experiments on face datasets clearly demonstrate that the proposed methods are more robust to illumination variations, corruptions, and occlusions.

RFID Estimation With Blocker Tags

With the increasing popularization of radio frequency identification (RFID) technology in the retail and logistics industry, RFID privacy concern has attracted much attention, because a tag responds to queries from readers no matter they are authorized or not. An effective solution is to use a commercially available blocker tag that behaves as if a set of tags with known blocking IDs are present. However, the use of blocker tags makes the classical RFID estimation problem much more challenging, as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose RFID estimation scheme with blocker tags (REB), the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the EPC C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. REB conducts statistical inference from the two sets of responses and estimates the number of genuine tags. Rigorous theoretical analysis of parameter settings is proposed to guarantee the required estimation accuracy, meanwhile minimizing the time cost and energy cost of REB. We also reveal a fundamental tradeoff between the time cost and energy cost of REB, which can be flexibly adjusted by the users according to the practical requirements. Extensive experimental results reveal that REB significantly outperforms the state-of-the-art identification protocols in terms of both time efficiency and energy efficiency.