&lt;title&gt;Image retrieval based on the directional edge similarity&lt;/title&gt;

Shanbehzadeh, Jamshid; Mahmoudi, Fariborz; Sarafzadeh, Abdolhosain; Moghadam, Amir Masoud Eftekhary

doi:10.1117/12.360430

Cited by 4 publications

(3 citation statements)

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“…We have used MATLAB 7 to implement the system and have run it on a Pentium IV processor. The feature extraction time for the entire database lasts 10 minutes which is surprisingly less than other presented methods using texture [3], [7] and [8]. For example, the method described in [3] consumes hours to extract feature vectors, as it applies wavelet transform on the entire image.…”

Section: Resultsmentioning

confidence: 99%

“…We have chosen N=10 to be able to compare the results with previously used methods. The weighted precision is a quantity to measure the rank of the relevant images and is obtained by the following equation: (7) where n k represents the number of relevant images within the first k retrieved images.…”

Section: Resultsmentioning

confidence: 99%

“…The performance of this approach is limited, because it does not consider the correlations between edges. Shanbehzadeh [7] improved this method and considered correlations between neighboring edges by using a weighted function. The result was a directional histogram of similar edges (DHSE).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Fast Content Based Color Image Retrieval System Based on Texture Analysis of Edge Map

Salehian

Zamani

Jamzad

2011

AMR

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In this paper we propose a method for CBIR based on the combination of texture, edge map and color. As texture of edges yields important information about the images, we utilized an adaptive edge detector that produces a binary edge image. Also, using the statistics of color in two different color spaces provides complementary information to retrieve images. Our method is time efficient since we have applied texture calculations on the binary edge image. Our experimental results showed both the higher accuracy and lower time complexity of our method with similar related works using SIMPLIcity database.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%