Image Retrieval Based on Color, Shape, and Texture for Ornamental Leaf with Medicinal Functionality Images

Arai, Kohei; Abdullah, Indra Nugraha; Okumura, Hiroshi

doi:10.5815/ijigsp.2014.07.02

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…But the fact is that for people in special situations, such as mentally handicapped people and their families, this is a very difficult dream to achieve [1]. Data show that there are about 11 million adults with autism and mental disorders in China, but the current national policy of rehabilitation assistance for disabled children covers only 0-6 years old, which has certain lag and limitations [2]. The rehabilitation of adults with mental disorders lacks policy and financial support.…”

Section: Introductionmentioning

confidence: 99%

Tower-type Advanced Energetic Rehabilitation Model for Adult Mental Disorder Persons

Wei¹

2019

Proceedings of the First International Symposium on Management and Social Sciences (ISMSS 2019)

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Adult mental handicapped people have significantly lower intelligence function than normal persons, accompanied by adaptive behavioral deficits. They are slower than their peers in learning and dealing with daily life, understanding of the surroundings and adaptability to the environment. If they are taught to master the skills of home life and employment, it is possible to integrate into society relatively smoothly. This paper focuses on the needs of adults with mental disorders, explores scientific and feasible community rehabilitation services, makes full use of community resources, and provides home life skills training, community integration and employment skills training, which is of great significance to help adults with mental disorders realize their life value and build a well-off society in an all-round way. The paper takes Wuhan Family Support Center for Mental Handicapped Persons named Beautiful Home as an example, uses case tracking method, utilizes the methods of text analysis, individual interviews and observation, and combines the concept of whole-person rehabilitation and the perspective of energetic theory, to analyze the practical effect and predicament of the institution. A towertype progressive energy-increasing rehabilitation model is extracted, which conforms to the rehabilitation law of adults with mental disorders and meets the actual needs.

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Section: Introductionmentioning

confidence: 99%

Tower-type Advanced Energetic Rehabilitation Model for Adult Mental Disorder Persons

Wei¹

2019

Proceedings of the First International Symposium on Management and Social Sciences (ISMSS 2019)

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“…Image retrieval system proposed by Kohei Arai et al [10] utilizes HSV-based color histogram, Zernike complex moments, and Dyadic wavelet transformation from leaf images as features.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Shape and Color Features for Classification of Four Paddy Varieties

Chaugule¹,

Mali²

2014

IJIGSP

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This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature set from the features was identified for accurate classification. The Shape-n-Color feature set outperformed in almost all the instances of classification.

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“…Content based image classification and retrieval is the popular research area in the recent years. For an image color, shape, texture are the features used to describe an image [5,6,7]. Then the images whose features match with that of the feature in the query are extracted from the database.…”

Section: Introductionmentioning

confidence: 99%

A Histogram-based Classification of Image Database Using Scale Invariant Features

Mishra¹,

Panda²

2017

IJIGSP

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Abstract-Development of advanced technology has increased the size of data and has also created different categories of data. Classifying these different categories of data is the need of the era. We have proposed a method of classifying the image database containing four categories of images like human face, airplane, cup and butterfly. Our approach involves steps like feature extraction, bag of feature creation, histogram representation and classification using decision tree. For feature extraction SIFT (Scale Invariant Feature Transform) algorithm is used since it is invariant to rotation, change of scale, illumination etc. After extracting the features the bag of features concept is used to group the features using k-means clustering algorithm. Then a histogram is plotted for each image in the image database which represents the distributions of data in different clusters. In the final step the most robust, simple and flexible decision tree algorithm is applied on the table created from the histogram plots to obtain the classification result. The experimental observations and the calculated accuracy proves that this method of classification works well for classifying an image dataset having different categories of images.

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Image Retrieval Based on Color, Shape, and Texture for Ornamental Leaf with Medicinal Functionality Images

Cited by 7 publications

References 12 publications

Tower-type Advanced Energetic Rehabilitation Model for Adult Mental Disorder Persons

Tower-type Advanced Energetic Rehabilitation Model for Adult Mental Disorder Persons

Evaluation of Shape and Color Features for Classification of Four Paddy Varieties

A Histogram-based Classification of Image Database Using Scale Invariant Features

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