Content-based image retrieval using visually significant point features

Banerjee, Minakshi; Kundu, Malay K.; Maji, Pradipta

doi:10.1016/j.fss.2009.02.024

Cited by 57 publications

(36 citation statements)

References 30 publications

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“…In order to compare our approach, some of other previous well-know approaches [18] and [19] were survived.…”

Section: Resultsmentioning

confidence: 99%

“…In [18], an approach is proposed for CBIR using visually significant point features. The authors [18], were evaluated their approach performance using simplicity dataset considering N=10, 20 and 40.…”

Section: Resultsmentioning

confidence: 99%

“…The authors [18], were evaluated their approach performance using simplicity dataset considering N=10, 20 and 40. The precision% is computed for each category separately.…”

Section: Resultsmentioning

confidence: 99%

“…The precision% is computed for each category separately. The average precision of their approach [18] is shown in the Table 2 and it was compared by our proposed approach.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Digital Image Retrieval Using Combination of Texture, Shape and Colour Features

Saberi

Tajeripour

Fekri-Ershad

2013

IJSCAI

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“…In order to compare our approach, some of other previous well-know approaches [18] and [19] were survived.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The authors [18], were evaluated their approach performance using simplicity dataset considering N=10, 20 and 40. The precision% is computed for each category separately.…”

Section: Resultsmentioning

confidence: 99%

“…The precision% is computed for each category separately. The average precision of their approach [18] is shown in the Table 2 and it was compared by our proposed approach.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Digital Image Retrieval Using Combination of Texture, Shape and Colour Features

Saberi

Tajeripour

Fekri-Ershad

2013

IJSCAI

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show abstract

“…In [28], local descriptors of color and texture were calculated from the color moments and moments on Gabor filter responses to facilitate retrieval. In [29], visually significant point features were chosen as image features, which were identified by a fuzzy set theoretic approach for image recognition. In [30], retrieval accuracy was improved by combining color layout descriptor and Gabor texture descriptor.…”

Section: Related Workmentioning

confidence: 99%

Content Based Image Recognition by Information Fusion with Multiview Features

Das¹,

Thepade²,

Ghosh³

2015

IJITCS

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Abstract-Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

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Rough‐Fuzzy Hybridization and Granular Computing

Maji

Pal

2012

Rough‐Fuzzy Pattern Recognition

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Content-based image retrieval using visually significant point features

Cited by 57 publications

References 30 publications

Digital Image Retrieval Using Combination of Texture, Shape and Colour Features

Digital Image Retrieval Using Combination of Texture, Shape and Colour Features

Content Based Image Recognition by Information Fusion with Multiview Features

Rough‐Fuzzy Hybridization and Granular Computing

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