Matching Shapes With Self-Intersections: Application to Leaf Classification

Mokhtarian, Farzin; Abbasi, Sadegh

doi:10.1109/tip.2004.826126

Cited by 119 publications

(56 citation statements)

References 26 publications

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“…All features are extracted from digital leaf image. Except one feature, all features can be extracted automatically [7]. Jyotismita Chaki, Ranjan Parekh [9].In this paper leaf recognition has been done by using shape analysis and feature extraction.…”

Section: Related Workmentioning

confidence: 99%

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Classification of Name Based On Leaf Recognition Using BT and ED Algorithm

A.M.Ravishankkar¹,

Mohanapriya²

2016

IJCATR

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ABSTRACT:The main purpose of this paper should be to show that the outer frame of a leaf and with the help of Back propagation Network is enough to give a reasonable statement about the species category is identified. Leaves Recognition is a neuronal network based java application/applet to recognize images of leaves using Back propagation Network. The intention is to give the user the ability to administrate a hierarchical list of images, where they can perform some sort of image using edge detection to identify the individual tokens of every image. The Thinning algorithm here is used to process the image recursively and minimizes the found lines to a one-pixel wide one by comparing the actual pixel situation with specific patterns can be identified and then minimizes it. The urgent situation is that due to environmental degradation and lack of awareness, many rare plant species are at the risk of extinction so it is necessary to keep record for plant protection. It focuses on using digital image processing for the purpose of automate classification and recognition of plants based on the images of the leaves. It help to protect the plant and mainly it is used for highly production of rare plant or herbal plant used for medical purpose. Efficacy of the proposed methods is studied by using two neural classifiers. These are neuro-fuzzy controller and a feed-forward back-propagation multi-layered perception to discriminate between 28 classes of leaves. The features have been applied individually as well as in combination to investigate how recognition accuracies can be improved with the help of B&T algorithm.

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Section: Related Workmentioning

confidence: 99%

“…All other behaviour of the network is specified by the normal mathematical principals of a back propagation network. This neuronal network adopts with three tier network structure including the input layer, hidden layer [7],output layer.…”

Section: Step 4: Neuronal Networkmentioning

confidence: 99%

Classification of Name Based On Leaf Recognition Using BT and ED Algorithm

A.M.Ravishankkar¹,

Mohanapriya²

2016

IJCATR

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“…Elaborate work has been done in the field of content based image retrieval for plant identification which use leaf for recognition purpose [1] [2] [7][10]. Du et.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shape based Descriptor for Plant Identification

Asrani¹,

Jain²

2014

IJCA

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Content based image retrieval is one of the most challenging field in which widespread research is been carried out. In this paper, a modified and effective shape based leaf image retrieval system is presented for leaf identification. The method proposed is an extension of centroid distance method. The major drawbacks of the centroid distance approach are identified and resolved in our proposed approach. This approach, called Quad Centroid Distance Variation (QCDV) uses the concept of shape based image retrieval approach. Four values are computed and used as the feature vectors. The proposed approach effectively manages the size of the feature vectors, is computationally simple and affine invariant. Experiments were conducted on Swedish Leaf Image Database (SLID). The results justify the superior performance of the method even though the feature vector representing the image is very small.

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“…Most of current foliage retrieval systems are based on shape analysis (Agarwal, et al (2006), Mokhtarian & Abbasi 2004, Weiss & Ray 2005, Gandhi 2002, Im, Hishida, & Kunii 1998, Saitoh & Kaneko 2000, Soderkvist 2001, Yahiaoui, Herve, & Boujemaa 2005. For example, in (Mokhtarian & Abbasi 2004) curvature scale space is proposed for shape analysis and applied to the classification of Chrysanthemum images.…”

Section: A Foliage Image Retrievalmentioning

confidence: 99%

“…For example, in (Mokhtarian & Abbasi 2004) curvature scale space is proposed for shape analysis and applied to the classification of Chrysanthemum images. Soderkvist (2001) used a combination of several shape cues for retrieval with the Swedish leaf database involving 15 species.…”

Section: A Foliage Image Retrievalmentioning

confidence: 99%

Shape Matching for Foliage Database Retrieval

Ling¹,

Jacobs²

Semantic Mining Technologies for Multimedia Databases

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Short overview of the Chapter-Computer-aided foliage image retrieval systems have the potential to dramatically speed up the process of plant species identification. Despite previous research, this problem remains challenging due to the large intra-class variability and inter-class similarity of leaves. This is particularly true when a large number of species are involved. In this chapter, we present a shape-based approach, the inner-distance shape context, as a robust and reliable solution. We show that this approach naturally captures part structures and is appropriate to the shape of leaves. Furthermore, we show that this approach can be easily extended to include texture information arising from the veins of leaves. We also describe a real electronic field guide system that uses our approach. The effectiveness of the proposed method is demonstrated in experiments on two leaf databases involving more than 100 species and 1000 leaves.

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Matching Shapes With Self-Intersections: Application to Leaf Classification

Cited by 119 publications

References 26 publications

Classification of Name Based On Leaf Recognition Using BT and ED Algorithm

Classification of Name Based On Leaf Recognition Using BT and ED Algorithm

A Novel Shape based Descriptor for Plant Identification

Shape Matching for Foliage Database Retrieval

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