Mobile Multimedia Communications. 7th International ICST Conference, MOBIMEDIA 2011, Cagliari, Italy, September 5-7, 2011, Revised Selected Papers

Research Article

Automatic Object Classification and Image Retrieval by Sobel Edge Detection and Latent Semantic Methods

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  • @INPROCEEDINGS{10.1007/978-3-642-30419-4_18,
        author={Vesna Zeljkovic and Pavel Praks},
        title={Automatic Object Classification and Image Retrieval by Sobel Edge Detection and Latent Semantic Methods},
        proceedings={Mobile Multimedia Communications. 7th International ICST Conference, MOBIMEDIA 2011, Cagliari, Italy, September 5-7, 2011, Revised Selected Papers},
        proceedings_a={MOBIMEDIA},
        year={2012},
        month={5},
        keywords={Object Classification Image Retrieval Sparse Image Representation SVD-free Latent Semantic Method Cosine Similarity Coefficient Sobel Edge Detector},
        doi={10.1007/978-3-642-30419-4_18}
    }
    
  • Vesna Zeljkovic
    Pavel Praks
    Year: 2012
    Automatic Object Classification and Image Retrieval by Sobel Edge Detection and Latent Semantic Methods
    MOBIMEDIA
    Springer
    DOI: 10.1007/978-3-642-30419-4_18
Vesna Zeljkovic1,*, Pavel Praks2,*
  • 1: New York Institute of Technology (NYIT)
  • 2: VSB-Technical University of Ostrava
*Contact email: dr.zeljkovic@gmail.com, pavel.praks@vsb.cz

Abstract

We perform in this paper a comparative study of ability of the proposed novel image retrieval algorithms to provide automated object classification invariant of rotation, translation and scaling. We analyze simple cosine similarity coefficient methods and the SVD-free Latent Semantic method with an alternative sparse representation of color images. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: i) the processing of the whole image and ii) the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as automated object classification tool without special image pre-processing.