CLOVER: A Mobile Content-Based Leaf Image Retrieval System. In this paper, we present an effective and robust leaf image retrieval system called CLOVER that works especially in the mobile environment. For the inquiry, users sketch or photograph a leaf using a PDA equipped with a digital camera, and then send it to a server. Most leaves tend to have similar color and texture, which makes shape-based image retrieval more effective than color-based image retrieval. In order to improve retrieval performance, we proposed a new shape representation scheme based on the well-known MPP algorithm. The new scheme can reduce the number of points to consider for matching. In addition, we proposed a new dynamic matching algorithm based on the Nearest Neighbor search to reduce the matching time. We implemented a prototype system that supports adaptive transmission of images over 802.11b wireless networks to mobile devices and demonstrate its effectiveness and scalability through various experimental results.

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References in zbMATH (referenced in 2 articles )

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  1. Wäldchen, Jana; Mäder, Patrick: Plant species identification using computer vision techniques: a systematic literature review (2018)
  2. Fotopoulou, F.; Laskaris, N.; Economou, G.; Fotopoulos, S.: Advanced leaf image retrieval via multidimensional embedding sequence similarity (MESS) method (2013) ioport