An efficient technique for retrieval of color images in large databases

Shrivastava, Nishant; Tyagi, Vipin

doi:10.1016/j.compeleceng.2014.11.009

Cited by 64 publications

(38 citation statements)

References 19 publications

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“…The Corel-1000 dataset is commonly used for image classification and retrieval [38,67,68]. Corel datasets consist of various image categories containing plain background images of complex objects.…”

Section: Results On Blobsmentioning

confidence: 99%

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Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

et al. 2020

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This article presents symmetry of sampling, scoring, scaling, filtering and suppression over deep convolutional neural networks in combination with a novel content-based image retrieval scheme to retrieve highly accurate results. For this, fusion of ResNet generated signatures is performed with the innovative image features. In the first step, symmetric sampling is performed on the images from the neighborhood key points. Thereafter, the rotated sampling patterns and pairwise comparisons are performed, which return image smoothing by applying standard deviation. These values of smoothed intensity are calculated as per local gradients. Box filtering adjusts the results of approximation of Gaussian with standard deviation to the lowest scale and suppressed by non-maximal technique. The resulting feature sets are scaled at various levels with parameterized smoothened images. The principal component analysis (PCA) reduced feature vectors are combined with the ResNet generated feature. Spatial color coordinates are integrated with convolutional neural network (CNN) extracted features to comprehensively represent the color channels. The proposed method is experimentally applied on challenging datasets including Cifar-100 (10), Cifar-10 (10), ALOT (250), Corel-10000 (10), Corel-1000 (10) and Fashion (15). The presented method shows remarkable results on texture datasets ALOT with 250 categories and fashion (15). The proposed method reports significant results on Cifar-10 and Cifar-100 benchmarks. Moreover, outstanding results are obtained for the Corel-1000 dataset in comparison with state-of-the-art methods.

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Section: Results On Blobsmentioning

confidence: 99%

“…For analysis, the resulting matrix is mapped with color histograms. Moreover, [38] presented a new retrieval system, which retrieves the images step by step. This technique is used to reduce the diversity of a dataset by eliminating irrelevant images at every step.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

et al. 2020

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“…Many indexing methods for CBIR are reported in the literature. However, a color histogram based technique reported in [5][6][7][8] proved to be an effective one through the years. It has been used in many works and, although it is Copyright © 2016 MECS I.J.…”

Section: Histogram Indexing Methodsmentioning

confidence: 99%

Effect of Reducing Colors Number on the Performance of CBIR System

Alasadi¹,

Wahid²

2016

IJIGSP

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Taking inspiration from the fact that a human can distinguish only a limited number of colors, reducing the number of colors is an interesting task to be incorporated in image retrieval systems that is based on using only the most discriminative colors, which most of the time yields better results. Accordingly, the main goal of this paper is to study the influence on performance of reducing the colors number contained in images. Accomplishing this task poses an extra overhead on the system, which requires more computation time, but, on the other hand, can accelerate the comparison process. Due to their popularity and success, we specifically concentrate this study on histogram indexing methods, using both Euclidean distance and histogram intersection to assess consequently the distance and the similarity between images. Some simple, pertinent ideas related to the way we compare a pair of images using Euclidean Distance are given in the end of the paper, supported by preliminary obtained results.

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“…However, a color histogram based technique reported in [5][6][7][8] one of the oldest methods, many researchers admit it as a basic method for CBIR. It has influenced many works, although, Histograms hold some major shortcomings [9][10][11].…”

Section: Histogram Indexing Methodsmentioning

confidence: 99%

Automatic Detection of Surface Defects on Citrus Fruit based on Computer Vision Techniques

S.H.¹,

C.J.²

2015

IJIGSP

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Abstract-Taking inspiration from the fact that a human can distinguish only a limited number of colors, reducing the number of colors is an interesting task to be incorporated in image retrieval systems that is based on using only the most discriminative colors, which most of the time yields better results.Accordingly, the main goal of this paper is to study the influence on performance of reducing the colors number contained in images. Accomplishing this task poses an extra overhead on the system, which requires more computation time, but, on the other hand, can accelerate the comparison process. Due to their popularity and success, we specifically concentrate this study on histogram indexing methods, using both Euclidean distance and histogram intersection to assess consequently the distance and the similarity between images. Some simple, pertinent ideas related to the way we compare a pair of images using Euclidean Distance are given in the end of the paper, supported by preliminary obtained results.

show abstract

An efficient technique for retrieval of color images in large databases

Cited by 64 publications

References 19 publications

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

Effect of Reducing Colors Number on the Performance of CBIR System

Automatic Detection of Surface Defects on Citrus Fruit based on Computer Vision Techniques

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