Fundamentals of Content-Based Image Retrieval

Long, Fuhui; Zhang, Hong-Jiang; Feng, David Dagan

doi:10.1007/978-3-662-05300-3_1

Cited by 337 publications

(208 citation statements)

References 50 publications

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“…В частности, к недостаткам многих объектов изме-рения с точки зрения обработки изображений мож-но отнести их несимметричность [6], нерегулярность (отсутствие повторяющихся элементов) [7], большой диапазон значений параметров [8] и большая скорость изменения значений параметров [9], что увеличивает и без того большую временнỳю сложность обработки.…”

Section: анализ литературных данных и постановка проблемыunclassified

The virtual model method in the metrological provision of automated design and control

Становский¹,

Bovnegra²,

Шихирева³

et al. 2015

EEJET

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Section: анализ литературных данных и постановка проблемыunclassified

The virtual model method in the metrological provision of automated design and control

Становский¹,

Bovnegra²,

Шихирева³

et al. 2015

EEJET

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“…To obtain significant results different methods are used by researchers to evaluate similarity. For example, some researchers used fuzzy measures [9], histogram intersection [8,24], and Euclidean distance [25]. In our proposed approach, Euclidean distance is used to compute the similarity between two feature vectors as follows…”

Section: Similarity Measurementioning

confidence: 99%

Content- based Image Retrieval Approach using Three Features Color, Texture and Shape

Jain¹,

Yadav²

2014

IJCA

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Content Based Image Retrieval is a technique of automatic indexing and retrieving of images from a large data base. Visual features such as color, texture and shape are extracted to differentiate images in Content Based Image Retrieval (CBIR). Each of the features can be represented using one or more feature descriptors. These features descriptors combined with form feature vectors and are used together. During the retrieval, features and descriptors of the query are compared with the available images in the database. The images are then retrieved from database on the basis of distance of their feature vectors. At present, information of the maximum two features have been utilized for comparing the image and these methods provides the less accurate result. In our proposed work, more than two features i.e. three features are used for comparison and retrieval of image from the database. These three features are color, shape & texture features for image retrieval and provide more accurate results. These features are combined to fulfil the aspect of retrieval in image. The proposed work uses HSI color information especially Hue value, Fuzzy C-Mean algorithm for shape representation and co-occurrence matrix is used for texture feature extraction.

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“…The most punctual utilization of the term CBIR in writing appears to have been utilized by writers in to portray their tests of programmed recovery of pictures from a database by shading and shape highlights [4]. There are four periods of recovers the pictures from the database in light of the geometric properties of the information question pictures limit [5].…”

Section: Introductionmentioning

confidence: 99%

Content Based Image Retrieval Using Error Diffusion Block Truncation Coding and SVM Features

2017

IJAERD

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Fundamentals of Content-Based Image Retrieval

Cited by 337 publications

References 50 publications

The virtual model method in the metrological provision of automated design and control

The virtual model method in the metrological provision of automated design and control

Content- based Image Retrieval Approach using Three Features Color, Texture and Shape

Content Based Image Retrieval Using Error Diffusion Block Truncation Coding and SVM Features

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