The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform

Allon G. Percus - One of the best experts on this subject based on the ideXlab platform.

  • ISMIS - Unsupervised Vehicle Recognition Using Incremental Reseeding of Acoustic Signatures
    Lecture Notes in Computer Science, 2018
    Co-Authors: Justin Sunu, Allon G. Percus, Blake Hunter
    Abstract:

    Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we treat the frequency signature in each time window as an Individual Data Point. We then use a spectral embedding for dimensionality reduction. Based on the leading eigenvectors, we relate the performance of an incremental reseeding algorithm to that of spectral clustering. We find that incremental reseeding accurately identifies Individual vehicles using their acoustic signatures.

  • Dimensionality reduction for acoustic vehicle classification with spectral embedding
    2018 IEEE 15th International Conference on Networking Sensing and Control (ICNSC), 2018
    Co-Authors: Justin Sunu, Allon G. Percus
    Abstract:

    We propose a method for recognizing moving vehicles, using Data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an Individual Data Point to be classified. By applying a spectral embedding, we decrease the dimensionality of the Data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

  • ICNSC - Dimensionality reduction for acoustic vehicle classification with spectral embedding
    2018 IEEE 15th International Conference on Networking Sensing and Control (ICNSC), 2018
    Co-Authors: Justin Sunu, Allon G. Percus
    Abstract:

    We propose a method for recognizing moving vehicles, using Data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an Individual Data Point to be classified. By applying a spectral embedding, we decrease the dimensionality of the Data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

  • Unsupervised vehicle recognition using incremental reseeding of acoustic signatures
    arXiv: Machine Learning, 2018
    Co-Authors: Justin Sunu, Blake Hunter, Allon G. Percus
    Abstract:

    Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we treat the frequency signature in each time window as an Individual Data Point. We then use a spectral embedding for dimensionality reduction. Based on the leading eigenvectors, we relate the performance of an incremental reseeding algorithm to that of spectral clustering. We find that incremental reseeding accurately identifies Individual vehicles using their acoustic signatures.

  • Dimensionality reduction for acoustic vehicle classification with spectral clustering
    arXiv: Machine Learning, 2017
    Co-Authors: Justin Sunu, Allon G. Percus
    Abstract:

    We propose a method for recognizing moving vehicles, using Data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an Individual Data Point to be classified. By applying a spectral embedding, we decrease the dimensionality of the Data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

Yang Yang - One of the best experts on this subject based on the ideXlab platform.

  • Robust Hashing With Local Models for Approximate Similarity Search
    IEEE Transactions on Systems Man and Cybernetics, 2014
    Co-Authors: Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
    Abstract:

    Similarity search plays an important role in many applications involving high-dimensional Data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the Data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional Data Points into binary hash codes by effectively utilizing local structural information. In RHLM, for each Individual Data Point in the training Dataset, a local hashing model is learned and used to predict the hash codes of its neighboring Data Points. The local models from all the Data Points are globally aligned so that an optimal hash code can be assigned to each Data Point. After obtaining the hash codes of all the training Data Points, we design a robust method by employing $\ell_{2,1}$ -norm minimization on the loss function to learn effective hash functions, which are then used to map each Database Point into its hash code. Given a query Data Point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life Datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

  • Robust Hashing With Local Models for Approximate Similarity Search
    IEEE Transactions on Cybernetics, 2014
    Co-Authors: Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
    Abstract:

    Similarity search plays an important role in many applications involving high-dimensional Data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the Data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional Data Points into binary hash codes by effectively utilizing local structural information. In RHLM, for each Individual Data Point in the training Dataset, a local hashing model is learned and used to predict the hash codes of its neighboring Data Points. The local models from all the Data Points are globally aligned so that an optimal hash code can be assigned to each Data Point. After obtaining the hash codes of all the training Data Points, we design a robust method by employing ℓ2,1-norm minimization on the loss function to learn effective hash functions, which are then used to map each Database Point into its hash code. Given a query Data Point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life Datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

Justin Sunu - One of the best experts on this subject based on the ideXlab platform.

  • ISMIS - Unsupervised Vehicle Recognition Using Incremental Reseeding of Acoustic Signatures
    Lecture Notes in Computer Science, 2018
    Co-Authors: Justin Sunu, Allon G. Percus, Blake Hunter
    Abstract:

    Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we treat the frequency signature in each time window as an Individual Data Point. We then use a spectral embedding for dimensionality reduction. Based on the leading eigenvectors, we relate the performance of an incremental reseeding algorithm to that of spectral clustering. We find that incremental reseeding accurately identifies Individual vehicles using their acoustic signatures.

  • Dimensionality reduction for acoustic vehicle classification with spectral embedding
    2018 IEEE 15th International Conference on Networking Sensing and Control (ICNSC), 2018
    Co-Authors: Justin Sunu, Allon G. Percus
    Abstract:

    We propose a method for recognizing moving vehicles, using Data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an Individual Data Point to be classified. By applying a spectral embedding, we decrease the dimensionality of the Data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

  • ICNSC - Dimensionality reduction for acoustic vehicle classification with spectral embedding
    2018 IEEE 15th International Conference on Networking Sensing and Control (ICNSC), 2018
    Co-Authors: Justin Sunu, Allon G. Percus
    Abstract:

    We propose a method for recognizing moving vehicles, using Data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an Individual Data Point to be classified. By applying a spectral embedding, we decrease the dimensionality of the Data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

  • Unsupervised vehicle recognition using incremental reseeding of acoustic signatures
    arXiv: Machine Learning, 2018
    Co-Authors: Justin Sunu, Blake Hunter, Allon G. Percus
    Abstract:

    Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we treat the frequency signature in each time window as an Individual Data Point. We then use a spectral embedding for dimensionality reduction. Based on the leading eigenvectors, we relate the performance of an incremental reseeding algorithm to that of spectral clustering. We find that incremental reseeding accurately identifies Individual vehicles using their acoustic signatures.

  • Dimensionality reduction for acoustic vehicle classification with spectral clustering
    arXiv: Machine Learning, 2017
    Co-Authors: Justin Sunu, Allon G. Percus
    Abstract:

    We propose a method for recognizing moving vehicles, using Data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an Individual Data Point to be classified. By applying a spectral embedding, we decrease the dimensionality of the Data sufficiently for K-nearest neighbors to provide accurate vehicle identification.

Jingkuan Song - One of the best experts on this subject based on the ideXlab platform.

  • Robust Hashing With Local Models for Approximate Similarity Search
    IEEE Transactions on Systems Man and Cybernetics, 2014
    Co-Authors: Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
    Abstract:

    Similarity search plays an important role in many applications involving high-dimensional Data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the Data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional Data Points into binary hash codes by effectively utilizing local structural information. In RHLM, for each Individual Data Point in the training Dataset, a local hashing model is learned and used to predict the hash codes of its neighboring Data Points. The local models from all the Data Points are globally aligned so that an optimal hash code can be assigned to each Data Point. After obtaining the hash codes of all the training Data Points, we design a robust method by employing $\ell_{2,1}$ -norm minimization on the loss function to learn effective hash functions, which are then used to map each Database Point into its hash code. Given a query Data Point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life Datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

  • Robust Hashing With Local Models for Approximate Similarity Search
    IEEE Transactions on Cybernetics, 2014
    Co-Authors: Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
    Abstract:

    Similarity search plays an important role in many applications involving high-dimensional Data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the Data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional Data Points into binary hash codes by effectively utilizing local structural information. In RHLM, for each Individual Data Point in the training Dataset, a local hashing model is learned and used to predict the hash codes of its neighboring Data Points. The local models from all the Data Points are globally aligned so that an optimal hash code can be assigned to each Data Point. After obtaining the hash codes of all the training Data Points, we design a robust method by employing ℓ2,1-norm minimization on the loss function to learn effective hash functions, which are then used to map each Database Point into its hash code. Given a query Data Point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life Datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

Zi Huang - One of the best experts on this subject based on the ideXlab platform.

  • Robust Hashing With Local Models for Approximate Similarity Search
    IEEE Transactions on Systems Man and Cybernetics, 2014
    Co-Authors: Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
    Abstract:

    Similarity search plays an important role in many applications involving high-dimensional Data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the Data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional Data Points into binary hash codes by effectively utilizing local structural information. In RHLM, for each Individual Data Point in the training Dataset, a local hashing model is learned and used to predict the hash codes of its neighboring Data Points. The local models from all the Data Points are globally aligned so that an optimal hash code can be assigned to each Data Point. After obtaining the hash codes of all the training Data Points, we design a robust method by employing $\ell_{2,1}$ -norm minimization on the loss function to learn effective hash functions, which are then used to map each Database Point into its hash code. Given a query Data Point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life Datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

  • Robust Hashing With Local Models for Approximate Similarity Search
    IEEE Transactions on Cybernetics, 2014
    Co-Authors: Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
    Abstract:

    Similarity search plays an important role in many applications involving high-dimensional Data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the Data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional Data Points into binary hash codes by effectively utilizing local structural information. In RHLM, for each Individual Data Point in the training Dataset, a local hashing model is learned and used to predict the hash codes of its neighboring Data Points. The local models from all the Data Points are globally aligned so that an optimal hash code can be assigned to each Data Point. After obtaining the hash codes of all the training Data Points, we design a robust method by employing ℓ2,1-norm minimization on the loss function to learn effective hash functions, which are then used to map each Database Point into its hash code. Given a query Data Point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life Datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.