Interest Point and Segmentation-Based Photo Annotation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Bálint Daróczy , István Petrás , András A. Benczúr , Zsolt Fekete , Dávid Nemeskey , Dávid Siklósi , Zsuzsa Weiner

ABSTRACT

Our approach to the ImageCLEF 2009 tasks is based on image segmentation, SIFT keypoints and Okapi BM25-based text retrieval. We use feature vectors to describe the visual content of an image segment, a keypoint or the entire image. The features include color histograms, a shape descriptor as well as a 2D Fourier transform of a segment and an orientation histogram of detected keypoints. We trained a Gaussian Mixture Model (GMM) to cluster the feature vectors extracted from the image segments and keypoints independently. The normalized Fisher gradient vector computed from GMM of SIFT descriptors is a well known technique to represent an image with only one vector. Novel to our method is the combination of Fisher vectors for keypoints with those of the image segments to improve classification accuracy. We introduced correlation-based combining methods to further improve classification quality. More... »

PAGES

340-347

References to SciGraph publications

  • 2010. Overview of the CLEF 2009 Large-Scale Visual Concept Detection and Annotation Task in MULTILINGUAL INFORMATION ACCESS EVALUATION II. MULTIMEDIA EXPERIMENTS
  • 2003. Term Proximity Scoring for Keyword-Based Retrieval Systems in ADVANCES IN INFORMATION RETRIEVAL
  • 2010. Diversity in Photo Retrieval: Overview of the ImageCLEFPhoto Task 2009 in MULTILINGUAL INFORMATION ACCESS EVALUATION II. MULTIMEDIA EXPERIMENTS
  • 2010. Overview of the WikipediaMM Task at ImageCLEF 2009 in MULTILINGUAL INFORMATION ACCESS EVALUATION II. MULTIMEDIA EXPERIMENTS
  • 2009. Evaluation of Diversity-Focused Strategies for Multimedia Retrieval in EVALUATING SYSTEMS FOR MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS
  • Book

    TITLE

    Multilingual Information Access Evaluation II. Multimedia Experiments

    ISBN

    978-3-642-15750-9
    978-3-642-15751-6

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_44

    DOI

    http://dx.doi.org/10.1007/978-3-642-15751-6_44

    DIMENSIONS

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