Generative part-based Gabor object detector

Ekaterina Riabchenko, Joni-Kristian Kämäräinen

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)


    Discriminative part-based models have become the approach for visual object detection. The models learn from a large number of positive and negative examples with annotated class labels and location (bounding box). In contrast, we propose a part-based generative model that learns from a small number of positive examples. This is achieved by utilizing "privileged information", sparse class-specific landmarks with semantic meaning. Our method uses bio-inspired complex-valued Gabor features to describe local parts. Gabor features are transformed to part probabilities by unsupervised Gaussian Mixture Model (GMM). GMM estimation is robustified for a small amount of data by a randomization procedure inspired by random forests. The GMM framework is also used to construct a probabilistic spatial model of part configurations. Our detector is invariant to translation, rotation and scaling. On part level invariance is achieved by pose quantization which is more efficient than previously proposed feature transformations. In the spatial model, invariance is achieved by mapping parts to an "aligned object space". Using a small number of positive examples our generative method performs comparably to the state-of-the-art discriminative method.

    Original languageEnglish
    Pages (from-to)1-8
    Number of pages8
    JournalPattern Recognition Letters
    Issue numberP1
    Publication statusPublished - 15 Dec 2015
    Publication typeA1 Journal article-refereed


    • Gabor feature
    • Gaussian mixture model
    • Generative learning
    • Object detection
    • Visual classification

    Publication forum classification

    • Publication forum level 2

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Signal Processing


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