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  • Object Detection via Structural Feature Selection and Shape Model

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    91329_1.pdf (8.841Mb)
    Author(s)
    Zhang, Huigang
    Bai, Xiao
    Zhou, Jun
    Cheng, Jian
    Zhao, Huijie
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2013
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    Abstract
    In this paper, we propose an approach for object detection via structural feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly use bounding boxes on objects. Our approach first builds a class-specific codebook of local contour features, and then generates structural feature descriptors by combining context shape information. These descriptors are robust to both within-class variations and scale changes. Through exploring pairwise image matching using fast earth mover's distance, feature weights can be iteratively updated. Those discriminative ...
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    In this paper, we propose an approach for object detection via structural feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly use bounding boxes on objects. Our approach first builds a class-specific codebook of local contour features, and then generates structural feature descriptors by combining context shape information. These descriptors are robust to both within-class variations and scale changes. Through exploring pairwise image matching using fast earth mover's distance, feature weights can be iteratively updated. Those discriminative foreground features are assigned high weights and then selected to build a part-based shape model. Finally, object detection is performed by matching each testing image with this model. Experiments show that the proposed method is very effective. It has achieved comparable performance to the state-of-the-art shape-based detection methods, but requires much less training information.
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    Journal Title
    IEEE Transactions on Image Processing
    Volume
    22
    Issue
    12
    DOI
    https://doi.org/10.1109/TIP.2013.2281406
    Copyright Statement
    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Computer Vision
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/56811
    Collection
    • Journal articles

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