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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Video

Updated: Apr 4, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
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Probability Models for Open Set Recognition.

Walter J Scheirer, Lalit P Jain, Terrance E Boult

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
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    Summary
    This summary is machine-generated.

    This study introduces compact abating probability (CAP) for open set recognition, improving multi-class classification with unknown inputs. A novel Weibull-calibrated SVM (W-SVM) further enhances performance in object detection and OCR tasks.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Real-world computer vision faces challenges with open set recognition, where systems must classify data with incomplete knowledge and unknown inputs.
    • Existing methods incorporate open space risk terms to address data beyond known class boundaries.
    • Extending open space risk limiting classification to non-linear, multi-class scenarios is an active research area.

    Purpose of the Study:

    • To introduce a novel open set recognition model, compact abating probability (CAP), designed for multi-class classification with unknown inputs.
    • To develop a new algorithm, Weibull-calibrated SVM (W-SVM), integrating statistical extreme value theory with support vector machines for improved open set recognition.
    • To evaluate the effectiveness of CAP models and the W-SVM algorithm on object detection and optical character recognition (OCR) tasks.

    Main Methods:

    • Developed the compact abating probability (CAP) model, where class membership probability diminishes as data points approach open space.
    • Introduced the Weibull-calibrated SVM (W-SVM) algorithm, combining CAP principles with Weibull distribution for score calibration and support vector machines.
    • Applied CAP models and W-SVM to multi-class classification, object detection, and OCR problems.

    Main Results:

    • CAP models demonstrated improved open set recognition performance across multiple algorithms.
    • The W-SVM algorithm significantly outperformed state-of-the-art methods in open set object detection and OCR tasks.
    • Statistical extreme value theory proved beneficial for score calibration in support vector machines for open set recognition.

    Conclusions:

    • The CAP formulation provides an effective approach to enhance open set recognition in computer vision.
    • The W-SVM algorithm represents a significant advancement for tackling open set recognition challenges, particularly in object detection and OCR.
    • Integrating statistical calibration methods with machine learning classifiers offers a promising direction for robust open set recognition systems.