Advanced leaf image retrieval via Multidimensional Embedding Sequence Similarity (MESS) method

Fotopoulou, Foteini; Laskaris, Nikolaos A.; Economou, George; Fotopoulos, S.

doi:10.1007/s10044-011-0254-6

Cited by 21 publications

(18 citation statements)

References 31 publications

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“…These facts were further confirmed by (Fotopoulou et al, 2011;Valliammal and Geethalakshmi, 2012) who stated in their publications that leaf image could be categorised based on colour, texture, shape or combination of these properties. Later, Zhang and Zhang (2008) was enhanced that the properties for these features such as surface area, surface perimeter and the disfigurement are inherited from the shape features, variance of red, green and blue channels are belonging to the colour features and texture energy, texture entropy and texture contrast are fitting to the texture features.…”

Section: Leaf Featuressupporting

confidence: 55%

“…On the other hand, (Valliammal and Geethalakshmi, 2012) used image segmentation for leaf feature extraction in order to locate object shape. Instead of segmentation, (Fotopoulou et al, 2011) was implemented Centroid Contour Distance (CCD) and Angle Code (CD) measurement for extracting the leaf edges. Although there are different approaches for extraction have been used, but they share a common goal, that is to extract the leaf features, as summarised in Example of leaf shape properties…”

Section: Jcsmentioning

confidence: 99%

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Leaf Features Extraction and Recognition Approaches to Classify Plant

Jabal

Hamid

Shuib

et al. 2013

Journal of Computer Science

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Plant classification based on leaf identification is becoming a popular trend. Each leaf carries substantial information that can be used to identify and classify the origin or the type of plant. In medical perspective, images have been used by doctors to diagnose diseases and this method has been proven reliable for years. Using the same method as doctors, researchers try to simulate the same principle to recognise a plant using high quality leaf images and complex mathematical formulae for computers to decide the origin and type of plants. The experiments have yielded many success stories in the lab, but some approaches have failed miserably when tested in the real world. This happens because researchers may have ignored the facts that the real world sampling may not have the luxury and complacency as what they may have in the lab. What this study intends to deliver is the ideal case approach in plant classification and recognition that not only applicable in the real world, but also acceptable in the lab. The consequence from this study is to introducing more external factors for consideration when experimenting real world sampling for leaf recognition and classification does this.

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Section: Leaf Featuressupporting

confidence: 55%

Section: Jcsmentioning

confidence: 99%

Leaf Features Extraction and Recognition Approaches to Classify Plant

Jabal

Hamid

Shuib

et al. 2013

Journal of Computer Science

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“…Most important information about the taxonomic identity for a plant is usually contained in its leaves [2]. Therefore, similar to most existing image-based plant identification [1]- [13] or retrieval [14]- [16] approaches, the proposed framework focuses on the recognition of leaf images which can be further applied to plant identification.…”

Section: Introductionmentioning

confidence: 97%

“…Similar to feature extraction from general images, a leaf image can be characterized by extracting its color [15], texture [1], [2], [15], and shape [1]- [16] features. Nevertheless, the color of a leaf may vary with the seasons and climatic conditions, while most species of leaves have similar colors.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of leaf image recognition with a novel learning-based approach

Hsiao

Chang

et al. 2014

2014 Science and Information Conference

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Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse codingbased framework can perform better.

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“…Several important parameters of the leaves that can be used for multimodal biometric analysis include the leaflet's basic geometric features [21], colour [22], texture [23], and shape [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

Makky

2016

International Journal on Advanced Science, Engineering and Information Technology

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Every plant has unique morphological features, and can be used for its characteristics identity, such as age. When the plants grow, their morphological features may change, observable visually or by optical equipment. These various morphology transformations were categorized as multi-modal Bio-metrics. In this study, tomatoes from local cultivar were grown in a net house, in west Sumatra. The growth medium comprised of soil, husk, and manures with the composition of 1: 1: 1 respectively. For best growth, plants were watered regularly, and protect from pests and weeds. The observations were performed on 21st, 42nd, and 63rd day after sowing (DAS). The samples were the leaflets of the primary compound leaves of the plants. The leaflets were cut and digitized using a high-resolution colour scanner. The imaging performed at 300 dpi resolution, and the recorded image subsequently processed by the image processing software. Image segmentation performed to remove background from the object. Furthermore, the greenish of leaf object in the image were measured in RGB colour space. The leaf dimensions and area were quantified by the software, as well as the length of the leaflet main vein at central axis. Two secondary leaflet's blades were selected manually, and the angle formed between the blades and the main vein was measured. A Statistical engineering program was used to identify the principal morphology characteristics of the leaf, by means of Principal component analysis (PCA). Mathematical models were developed based on the principal component values and leaflets position to determine the plants age and state. Results showed all model have coefficient of correlation higher than 0.99 indicating acceptable accuracy.

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Advanced leaf image retrieval via Multidimensional Embedding Sequence Similarity (MESS) method

Cited by 21 publications

References 31 publications

Leaf Features Extraction and Recognition Approaches to Classify Plant

Leaf Features Extraction and Recognition Approaches to Classify Plant

Comparative study of leaf image recognition with a novel learning-based approach

Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

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