content based image retrieval using embedded neural networks with bandletized regions
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ID: 147872
2015
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Abstract
One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.
| Reference Key |
ashraf2015entropycontent
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|---|---|
| Authors | ;Rehan Ashraf;Khalid Bashir;Aun Irtaza;Muhammad Tariq Mahmood |
| Journal | European journal of medicinal chemistry |
| Year | 2015 |
| DOI |
10.3390/e17063552
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| URL | |
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