&lt;title&gt;Image retrieval based on color features: an evaluation study&lt;/title&gt;

Zhang, Hong-Jiang; Gong, Yi; Low, Chien Yong; Smoliar, Stephen W.

doi:10.1117/12.227244

Cited by 48 publications

(28 citation statements)

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“…Filter methods 16,27,29 determine the relevance of features to describe the data using statistical techniques and do not use feedback from subsequent learning or classification algorithms. Conversely, wrapper methods 14,17,26 use feedback that incorporate a Maximum Likelihood (ML) approach where weight assignment is optimized using classification accuracy as a performance measure.…”

Section: Feature Weighting Methodsmentioning

confidence: 99%

Feature Weighting and Retrieval Methods for Dynamic Texture Motion Features

Rahman¹,

Murshed²

2009

IJCIS

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Feature weighing methods are commonly used to find the relative significance among a set of features that are effectively used by the retrieval methods to search image sequences efficiently from large databases. As evidenced in the current literature, dynamic textures (image sequences with regular motion patterns) can be effectively modelled by a set of spatial and temporal motion distribution features like motion co-occurrence matrix. The aim of this paper is to develop effective feature weighting and retrieval methods for a set of dynamic textures while characterized by motion co-occurrence matrices.

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Section: Feature Weighting Methodsmentioning

confidence: 99%

Feature Weighting and Retrieval Methods for Dynamic Texture Motion Features

Rahman¹,

Murshed²

2009

IJCIS

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“…Image retrieval by segmenting them had been the focus of few research papers such as [15] and [16]. A detailed overview on the various literatures that are available on CBIR can be found in [17] and [18]. A discussion on various similarity measurement techniques can be found in [19].…”

Section: Related Workmentioning

confidence: 99%

Content Based Image Retrieval Using Colour Strings Comparison

Jenni

Mandala

Sunar

2015

Procedia Computer Science

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Content Based Image Retrieval (CBIR) is a technique that enables a user to extract an image based on a query, from a database containing a large amount of images. A very fundamental issue in designing a content based image retrieval system is to select the image features that best represent the image contents in a database. In this paper, our proposed method mainly concentrated on database classification and efficient image representation. We present a method for content based image retrieval based on support vector machine classifier. In this method the feature extraction was done based on the colour string coding and string comparison. We succeed in transferring the images retrieval problem to strings comparison. Thus the computational complexity is decreases obviously. The image database used in our experiment contains 1800 colour images from Corel photo galleries. This CBIR approach has significantly increased the accuracy in obtaining results for image retrieval. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific committee of 2nd International Symposium on Big Data and Cloud Computing (ISBCC'15).

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“…This leads to more computational time, inefficient indexing, and low performance. To overcome these problems, use of SVD [16], dominant color regions approach [19,20], and color clustering [21,22] have been proposed. The work in [23] presents a scheme for indexing and retrieval of color image data.…”

Section: Introductionmentioning

confidence: 99%