A Survey on Efficient Object Localization and Image Classification for Thumbnail Browsing

Abstract

Nowadays, with an increasing demand of advanced intelligent systems, computer vision applications dealing with foreground objects are becoming more challenging. The common assumption taken into consideration in most of the cases is that an image contains more than one object which produces undesired results when noticeable objects do not appear in the image. Our visual attention model originates from a well-known property of the human visual system that the human visual perception is highly adaptive and sensitive to structural information in images rather than nonstructural information. In this paper, we not only address the problem of ascertaining the existence of objects in an image but also discuss various methods for object detection. The input image is divided into non-overlapping patches, then the patches are categorized into different classes, like natural, man-made, and object to estimate the existence of the object. This paper provides a systematic review of algorithms and performance measures and assesses their effectiveness via metrics.

Authors and Affiliations

P. S. Korhale, P. S. Deshpande

Keywords

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  • EP ID EP19702
  • DOI -
  • Views 247
  • Downloads 5

How To Cite

P. S. Korhale, P. S. Deshpande (2015). A Survey on Efficient Object Localization and Image Classification for Thumbnail Browsing. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 3(3), -. https://europub.co.uk/articles/-A-19702