Visual category detection: an experimental perspective

Ville Viitaniemi

    Research output: ThesisDoctoral ThesisCollection of Articles


    Nowadays huge volumes of digital visual data are constantly being produced and archived. Automatically distilling useful information from such information masses requires one to somehow answer the grand long-standing question of computer vision: how to make computers understand images? In this thesis the visual content analysis problem is looked at as a category detection problem. In the category detection formulation, the general image content understanding task is partitioned into a number of small binary decision tasks. In each of the sub-tasks, one decides whether an image belongs to some pre-defined category. A category could be defined, for example, to consist of images taken indoors. By defining an appropriate set of categories, the visual content of an image can be described on a desired level of granularity by determining the image's membership in each one of the categories. This thesis discusses a framework for visual category detection that consists of three major components: feature extraction, feature-wise detection and fusion of the detection results. The point of view in the discussion is empirical: the framework is validated by the good levels of performance systems implementing it have demonstrated in various benchmark tasks of visual analysis. A body of experiments is described that compare various technological alternatives for implementing the three major components of the framework. In addition to comparing implementation techniques, the experiments demonstrate that the discussed generic category detection architecture is very versatile: a set of diverse visual analysis problems can be addressed using the same visual category detection system as a backbone component by equipping the system with a task-specific front-end. From the experiments and discussion in the thesis, one can conclude that the category detection formulation is a useful way of approaching the general image content understanding problem. In category detection, performances reaching the state-of-the-art can be realised using the presented fusion-based system architecture and implementation technologies of the system components.
    Translated title of the contributionVisuaalisten kategorioiden tunnistaminen: kokeellinen näkökulma
    Original languageEnglish
    QualificationDoctor's degree
    Awarding Institution
    • Aalto University
    • Oja, Erkki, Supervising Professor
    • Laaksonen, Jorma, Thesis Advisor
    Print ISBNs978-952-60-4585-6
    Electronic ISBNs978-952-60-4586-3
    Publication statusPublished - 2012
    MoE publication typeG5 Doctoral dissertation (article)


    • computer vision
    • image analysis
    • visual category
    • feature fusion
    • local image descriptor


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