A Survey On: “Content Based Image Retrieval Systems”

Singhai, Nidhi; Shandilya, Shishir Kumar

doi:10.5120/802-1139

Cited by 99 publications

(47 citation statements)

References 33 publications

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“…This histogram is the most used histogram in computer graphics and it uses its red, green and blue components to create a new color [5]. This color histogram combines red, green and blue colors to form a new color.…”

Section: Rgb Histogrammentioning

confidence: 99%

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Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Figueroa

Bykbaev

2012

ings

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In this paper we present the first stage of a new approach to improve the precision and recall of the content-based image retrieval task. To do this, we aim to combine three colour features, RGB and HSV histograms, and MPEG-7 Colour Layout Descriptor. To perform the combination, we propose to use an approximation based on Borda Voting-Schemes. Under that the Borda Voting-Schemes needs at least three votes to perform the combination, we intend to use the K-Nearest Neighbors methods to select the candidate images, given a query image. In the second stage, we'll implement our approach using at least three image databases.Keywords: HSV histogram, RGB histogram, Colour layout fescriptor, Borda Voting Schemes, KNN. ResumenEn este artículo se presenta la primera fase de experimentación de una nueva propuesta para mejorar la precisión y cobertura de la recuperación de imágenes basada en contenidos. Para realizar esta tarea, se propone combinar tres características de color, como son los histogramas RGB y HSV y el Descriptor de Distribución de Color del estándar MPEG-7. A fin de llevar a cabo la combinación, se plantea emplear una aproximación basada en Esquemas de Votación Borda. En virtud de que dichos esquemas requieren al menos tres votos, se propone usar la técnica de los K -Vecinos más cercanos, con el objetivo de seleccionar las imágenes candidatas a partir de imagen de consulta. En la segunda etapa se implementará nuestra propuesta empleando al menos tres bases de datos de imágenes.

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Section: Rgb Histogrammentioning

confidence: 99%

“…The constant growth of the visual information, like pictures and videos, makes necessary to have efficient methods to retrievethis kind of information in a proper way [5].…”

Section: Introductionmentioning

confidence: 99%

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Figueroa

Bykbaev

2012

ings

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“…In content based retrieval [2] the query is in the form of image and its features are used as the content describing it. These features are calculated for the query image as well as for all the images in the database using feature extraction methods.…”

Section: Content Based Retrievalmentioning

confidence: 99%

Content based Trademark Retrieval by Integrating Shape with Colour and Texture Information

Nigam¹,

Garg²,

Tripathi³

2011

IJCA

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Trademark is one of the most disputed items in Intellectual Property Rights. To grant a trademark, litigation is carried out to investigate if the new trademark applied for matches substantially with an already issued trademark. Considering the fact that the trademark database is very huge and keeps on increasing constantly, the manual search is definitely not a correct solution and an Information and Communication Technology (ICT) based system is required to automate this process.In a content based retrieval system, shape is easily the most important feature for the trademark image. Using shape feature various systems have been developed in the past. Here, a new approach is proposed that integrates shape with color and texture information that improves the results significantly in terms of Precision and Recall. A flexible combination of features is used for improving performance and clustering is applied to the database to enhance retrieval efficiency. General TermsTrademark, Content Based Image Retrieval.

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“…Many techniques to extract the image feature is proposed [6] [7][8] [9]. Shape is one of important visual feature of an image and used to describe image content [8].…”

Section: Introductionmentioning

confidence: 99%

Content Based Image Retrieval by using Multi Layer Centroid Contour Distance

Arai¹,

Rahmad²

2013

IJARAI

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Abstract-In this paper we present a new approach to measuring similarity between two shape of object. In conventional method, centroid contour distance (CCD) is formed by measuring distance between centroid (center) and boundary of object, but this method cannot capture if an object have multiple boundary in the same angle. We develop a novel approach feature shape by measuring distance between centroid (center) and boundary of object that can capture multiple boundaries in the same angle or multi-layer centroid contour distance (MLCCD). The experiment result on simulation dataset and plankton dataset show that the proposed method (MLCCD) better than the conventional method (CCD).

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A Survey On: “Content Based Image Retrieval Systems”

Cited by 99 publications

References 33 publications

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

Content based Trademark Retrieval by Integrating Shape with Colour and Texture Information

Content Based Image Retrieval by using Multi Layer Centroid Contour Distance

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