Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 9, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Discriminative BoW framework for mobile landmark recognition.

Tao Chen, Kim-Hui Yap

    IEEE Transactions on Cybernetics
    |July 13, 2013
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

    Optics letters·2024
    Same author

    High dimensional optical data - varifocal multiview imaging, compression and evaluation.

    Optics express·2023
    Same author

    Contrast Invariant Interest Point Detection by Zero-Norm LoG Filter.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2015
    Same author

    PoseShop: human image database construction and personalized content synthesis.

    IEEE transactions on visualization and computer graphics·2012
    Same author

    Designing aligned inorganic nanotubes at the electrode interface: towards highly efficient photovoltaic wires.

    Advanced materials (Deerfield Beach, Fla.)·2012
    Same author

    Further stratification of patients with multiple myeloma by International Staging System in combination with ratio of serum free κ to λ light chains.

    Leukemia & lymphoma·2012
    Same journal

    Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

    IEEE transactions on cybernetics·2026
    Same journal

    An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

    IEEE transactions on cybernetics·2026
    Same journal

    A Quantum Self-Attention Neural Network Model on Quantum Circuits.

    IEEE transactions on cybernetics·2026
    Same journal

    Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

    IEEE transactions on cybernetics·2026
    Same journal

    A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

    IEEE transactions on cybernetics·2026
    Same journal

    Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

    IEEE transactions on cybernetics·2026
    See all related articles

    This study introduces a novel soft bag-of-words (BoW) method for mobile landmark recognition. It effectively utilizes discriminative information from all image patches for improved recognition accuracy.

    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Conventional bag-of-words (BoW) methods treat image patches equally, potentially missing discriminative information.
    • Existing methods using patch discriminative information often discard potentially useful data through hard selection.

    Purpose of the Study:

    • To propose a new discriminative soft BoW method for enhanced mobile landmark recognition.
    • To leverage discriminative information from image patches, images, and codewords effectively.

    Main Methods:

    • A novel discriminative soft BoW approach is presented, learning at patch, image, and codeword levels.
    • Patch discriminative information is incorporated via vector quantization to create soft BoW histograms.
    • An ensemble of fuzzy support vector machine classifiers is trained using these histograms.

    More Related Videos

    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
    05:57

    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

    Published on: April 8, 2019

    Related Experiment Videos

    Last Updated: May 9, 2026

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
    03:31

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

    Published on: December 15, 2023

    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
    05:57

    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

    Published on: April 8, 2019

    Main Results:

    • The proposed method demonstrates effectiveness in mobile landmark recognition tasks.
    • Experimental results on two datasets validate the approach's performance.

    Conclusions:

    • The discriminative soft BoW method offers an improved approach to mobile landmark recognition.
    • Learning discriminative information across multiple levels enhances recognition capabilities.