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Use of Stochastic Optimization Algorithms in Image Retrieval Problems

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Computational Intelligence in Image Processing

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Abstract

In this chapter, the use of stochastic and evolutionary optimization techniques for image retrieval is addressed. We provide background and motivations of such approaches, as well as an overview of some of the most interesting ideas proposed in the recent literature in the field. The relevant methodologies refer to different applications of the optimization process, as a way of either improving the parameter setting in traditional retrieval tools, or directly classifying images within a dataset. Also, the use of stochastic optimization approaches based on social behaviors is discussed, showing how these approaches can be used to capture the semantics of a query through the interaction with the user. Strengths and weaknesses of different solutions are discussed, taking into account also implementation issues, complexity, and open directions of the research.

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Authors and Affiliations

  1. DISI, University of Trento, Via Sommarive 14, Trento, 38123 Povo, Italy

    Mattia Broilo & Francesco G. B. De Natale

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  1. Mattia Broilo
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  2. Francesco G. B. De Natale
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Correspondence to Francesco G. B. De Natale .

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  1. , Electrical Engineering Department, Jadavpur University, Kolkata, 700032, West Bengal, India

    Amitava Chatterjee

  2. , LISSI, University of Paris-Est Créteil, 61 avenue du Général de Gaulle, Créteil, 94010, France

    Patrick Siarry

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Broilo, M., De Natale, F.G. (2013). Use of Stochastic Optimization Algorithms in Image Retrieval Problems. In: Chatterjee, A., Siarry, P. (eds) Computational Intelligence in Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30621-1_11

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  • DOI: https://doi.org/10.1007/978-3-642-30621-1_11

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