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Updated: Jan 20, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
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Polyhedral Conic Classifiers for Computer Vision Applications and Open Set Recognition.

Hakan Cevikalp, Halil Saglamlar

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 20, 2019
    PubMed
    Summary
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    This study introduces novel quasi-linear discriminants that enhance object detection and open set recognition. These methods outperform existing techniques by better handling imbalanced and asymmetric data, improving classification accuracy.

    Area of Science:

    • Computer Science
    • Machine Learning
    • Pattern Recognition

    Background:

    • Object detection and open set recognition face challenges with numerically imbalanced and geometrically asymmetric data.
    • Existing large-margin methods struggle with datasets where positive samples are rare and negatives are diverse.

    Purpose of the Study:

    • To develop a new family of quasi-linear discriminants for improved performance in object detection and open set recognition.
    • To create discriminants that tightly circumscribe positive classes while accounting for overlapping negative classes.

    Main Methods:

    • Proposed a family of "polyhedral conic" discriminants with distorted L1 or L2 balls as positive regions.
    • Integrated the classification loss into deep neural networks for end-to-end feature and classifier learning.

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  • Utilized constrained quadratic programs, similar to Support Vector Machines (SVMs), for training.

    • Main Results:

    • The proposed discriminants significantly outperform linear SVMs, deep neural networks with softmax loss, and existing one-class discriminants.
    • Achieved superior results in object detection, face verification, open set recognition, and closed-set classification tasks.
    • Demonstrated comparable properties and run-time complexities to linear SVMs.

    Conclusions:

    • The novel quasi-linear discriminants offer a robust solution for classification tasks with imbalanced and asymmetric data.
    • End-to-end learning with deep neural networks further boosts classification accuracy.
    • These methods provide a significant advancement over current state-of-the-art techniques in visual recognition.