Wei Li

Bio: Wei Li is an academic researcher from Xiamen University of Technology. The author has contributed to research in topics: Granular computing & Fuzzy logic. The author has an hindex of 5, co-authored 20 publications receiving 134 citations. Previous affiliations of Wei Li include Xiamen University.

Boosted K-nearest neighbor classifiers based on fuzzy granules

Abstract: K-nearest neighbor (KNN) is a classic classifier, which is simple and effective. Adaboost is a combination of several weak classifiers as a strong classifier to improve the classification effect. These two classifiers have been widely used in the field of machine learning. In this paper, based on information fuzzy granulation, KNN and Adaboost, we propose two algorithms, a fuzzy granule K-nearest neighbor (FGKNN) and a boosted fuzzy granule K-nearest neighbor (BFGKNN), for classification. By introducing granular computing, we normalize the process of solving problem as a structured and hierarchical process. Structured information processing is focused, so the performance including accuracy and robust can be enhanced to data classification. First, a fuzzy set is introduced, and an atom attribute fuzzy granulation is performed on samples in the classified system to form fuzzy granules. Then, a fuzzy granule vector is created by multiple attribute fuzzy granules. We design the operators and define the measure of fuzzy granule vectors in the fuzzy granule space. And we also prove the monotonic principle of the distance of fuzzy granule vectors. Furthermore, we also give the definition of the concept of K-nearest neighbor fuzzy granule vector and present FGKNN algorithm and BFGKNN algorithm. Finally, we compare the performance among KNN, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Logistic Regression (LR), FGKNN and BFGKNN on UCI data sets. Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.

Using Granule to Search Privacy Preserving Voice in Home IoT Systems

Abstract: The Home IoT Voice System (HIVS) such as Amazon Alexa or Apple Siri can provide voice-based interfaces for people to conduct the search tasks using their voice. However, how to protect privacy is a big challenge. This paper proposes a novel personalized search scheme of encrypting voice with privacy-preserving by the granule computing technique. Firstly, Mel-Frequency Cepstrum Coefficients (MFCC) are used to extract voice features. These features are obfuscated by obfuscation function to protect them from being disclosed the server. Secondly, a series of definitions are presented, including fuzzy granule, fuzzy granule vector, ciphertext granule, operators and metrics. Thirdly, the AES method is used to encrypt voices. A scheme of searchable encrypted voice is designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granule of the voice. The experiments are conducted on corpus including English, Chinese and Arabic. The results show the feasibility and good performance of the proposed scheme.

Real-time detection of moving objects in a video sequence by using data fusion algorithm

Abstract: The moving object detection and tracking technology has been widely deployed in visual surveillance for security, which is, however, an extremely challenge to achieve real-time performance owing to...

Learning Deep Representations for Ground to Aerial Geolocalization (Open Access)

Abstract: : The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or bird's eye imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Abstract: In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.

Extension of rough set under incomplete information systems

Abstract: The classical rough set theory developed by professor Pawlak is based on complete information systems. It classifies objects using upper-approximation and lower-approximation defined on an indiscernibility relation that is a kind of equivalent relation. In order to process incomplete information systems, the classical rough set theory needs to be extended, especially, the indiscernibility relation needs to be extended to some inequivalent relation. There are several extensions for the indiscernibility relation now, such as tolerance relation, non-symmetric similarity relation, and valued tolerance relation. Unfortunately, these extensions have their own limitation. Presented in this paper is a new extension of rough set based on limited tolerance relation. The performances of these extended rough set models are also compared.