Content based image retrieval for leaf identification using structural features and neural networks

Keni, Nishant Deepak; Ansari, Rizwan Ali

doi:10.1109/spin.2017.8049963

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Studies Flavia dataset [49] [8], [17], [23], [25], [29], [36], [43] ICL leaf dataset [22] [46] LeafSnap dataset [21] [19] Own-authored dataset [1], [3], [10], [13], [26], [29], [35], [42] UCI Iris dataset [9] [26] UCI Leaf dataset [38] [2], [27], [30], [41], [51], [44] UCI One-hundred plant species leaves dataset [26] [20], [51] Uninformed [4], [31] We realized that a wide variety of datasets was used in the selected studies, and many authors chose to create their own datasets (8 studies of 25), possibly by not finding available datasets with the desired characteristics or information. Even so, public datasets were also widely used, with Flavia dataset [49], and UCI Leaf Dataset [38] being the most used (7 and 6 studies of 25, respectively).…”

Section: Datasetmentioning

confidence: 99%

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Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Ascari

Casanova

Fávero

et al. 2023

Anais Do XIV Computer on the Beach - COTB'23

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Plant species identification is a task of interdisciplinary interest,desirable in many contexts, such as gardening, botanical research,and agriculture. For some plant species such as the Acer Palmatum,the characteristics of leaves, petioles, and trunks can drasticallyvary among the different genera of the same subspecies. ComputerVision and Machine Learning research areas made possible thecreation of different classifiers trained to assist in species plantrecognition based on digital images. However, the success of thetraining of a classification model is directly linked to the qualityand adequacy of the dataset used. For the classification of AcerPalmatum plants, datasets composed of samples regarding the differentvarieties within this genus were not identified. Thus, in thispaper, we proposed the creation of a new dataset and of a classifierto support the identification of distinct plant genera of the subspeciesAcer Palmatum. We believe that our proposal aggregatesrelevant information not currently available, and will encouragefurther work aimed at automatically classifying between genera ofsome plant species which task is considered non-trivial even forexperienced growers.

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Section: Datasetmentioning

confidence: 99%

“…[29] Multi-Class Kernel Support Vector Machine (SVM) [36] Multilayer Perceptron (MLP) [25], [27] Neural Network using radial basis function [17] Neural Network (NN)…”

Section: Machine Learning Algorithms Used For Species Classificationmentioning

confidence: 99%

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Ascari

Casanova

Fávero

et al. 2023

Anais Do XIV Computer on the Beach - COTB'23

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“…The development of Internet technology has diversified the tourist demand for information services of scenic spots. More and more tourists are acquiring scenic spot information from the Internet, and actively sharing their travel photos with others [1][2][3][4]. Many travel photos contain the visual features of scenic spots, which reflect the background knowledge of the scenic spots and provide a wealth of relevant information (e.g.…”

Section: Introductionmentioning

confidence: 99%

Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing

Bai¹,

Han²

2020

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The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT) features. The SIFT feature extraction includes five steps: scale space construction, local space extreme point detection, precise positioning of key points, determination of key point size and direction, and generation of SIFT descriptor. Next, multiple correlated images were mined for the target scenic spot image, and the feature matching method between the target image and the set of scenic spot images was introduced in details. On this basis, a tourist behavior recognition method was designed based on temporal and spatial consistency. The proposed method was proved effective through experiments. The research results provide theoretical reference for image retrieval and behavior recognition in many other fields.

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Accuracy Analysis on Images Retrieval System using Radial Basis Function Algorithm and Coefficient Correlation

Sinuraya

Suwilo

Lydia

2020

2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)

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Content based image retrieval for leaf identification using structural features and neural networks

Cited by 3 publications

References 11 publications

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing

Accuracy Analysis on Images Retrieval System using Radial Basis Function Algorithm and Coefficient Correlation

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