An Efficient Method for Noisy Cell Image Segmentation Using Generalized α-Entropy

Sadek, Samy; Al-Hamadi, Ayoub; Michaelis, Bernd; Sayed, Usama

doi:10.1007/978-3-642-10546-3_5

Cited by 2 publications

(1 citation statement)

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“…Only a few segmentation algorithms combine multiple visual features (Deng and Manjunath, 2001;Carson et al, 2002;Kato and Pong, 2006;Kumar et al, 2008). In addition, image segmentation is known to be a time-or storage-consuming process (Sadek et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

An Innovative Multiple-Object Image Retrieval Framework Using Hierarchical Region Tree

Chen

Zhang

2013

International Journal of Multimedia Data Engineering and Management

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Inaccurate image segmentation often has a negative impact on object-based image retrieval. Researchers have attempted to alleviate this problem by using hierarchical image representation. However, these attempts suffer from the inefficiency in building the hierarchical image representation and the high computational complexity in matching two hierarchically represented images. This paper presents an innovative multiple-object retrieval framework named Multiple-Object Image Retrieval (MOIR) on the basis of hierarchical image representation. This framework concurrently performs image segmentation and hierarchical tree construction, producing a hierarchical region tree to represent the image. In addition, an efficient hierarchical region tree matching algorithm is designed for multiple-object retrieval with a reasonably low time complexity. The experimental results demonstrate the efficacy and efficiency of the proposed approach.

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