Analysis of image similarity with CBIR concept using wavelet transform and threshold algorithm

Rangkuti, Abdul Haris; Hakiem, Nashrul; Bahaweres, Rizal Broer; Harjoko, Agus; Putro, Agfianto Eko

doi:10.1109/isci.2013.6612388

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…Based on previous research in the similarity of the image to the search process based on shape and color, a sequencing process is based on the threshold value of the sample image through the use of the threshold algorithm [9], and get the comparison between the threshold value and aggregation value almost the same. Conversely, if it approaches 0, the comparison becomes very different.…”

Section: Prototype Of Image Similarity Testmentioning

confidence: 99%

Searching Similarity Digital Image Using Color Histogram

Widiyanto¹,

Kusrini²,

Fatta³

2019

techno

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In the era of globalization and modernization, as now, information technology is widely used in the fields of education, trade, animal husbandry, agriculture and even to the legal sector. One branch of science in the field of information technology that is growing rapidly is computer vision. One of the important roles of computer vision in everyday life is the use of computer vision. This can be applied in terms of face recognition, object detection, and can be applied to group images based on the order of similarity of the image, the ability of computer vision is applied to facilitate human work in selecting from several images to find the most similar images. In this study described the process of finding the similarity of an image with other images through several stages of research flow, the method used is to use RGB values that have been converted to grayscale, then the eucludian distance distance is calculated to determine the value of proximity of an image while calculating performance accuracy algorithm using confusion matrix. The search trial process resulted in an accuracy rate of 0.42, precision of 0.42 and recall 1 of 1000 datasets and 30 random data were taken. Found images that differ in color and shape but when converted into histograms the data has a fairly high similarity to the query. The disadvantage of this research is that images that have histograms similar to queries are displayed as similar images even though the reality is that images are very different from colors and shapes.Keywords: computer vision, similiarity, eucludian distance, grayscale, histogram

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Section: Prototype Of Image Similarity Testmentioning

confidence: 99%

Searching Similarity Digital Image Using Color Histogram

Widiyanto¹,

Kusrini²,

Fatta³

2019

techno

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“…The LL, HH, HL and LH frequency bands contain the approximation, diagonal, horizontal and vertical details of the signal. The LL band is decomposed again into LL1, LH1, HL1 and HH1 [15]. Here, we used two level discrete wavelet transform for evaluating the texture information of image.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

A Refined Approach of Image Retrieval Using RBF-SVM Classifier

Ansari¹,

Dixit²

2017

IJSIP

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“…In order to search and manage this data, there is strong need to index or categorize these images using proper system. Searching images on the basis of similarity can be used in medicine, arts, industry [1], security, military and many other areas [2]. This work deals with an image categorization and search on the basis of content.…”

Section: Introductionmentioning

confidence: 99%

Image Search Using Similarity Measures Based on Circular Sectors

Masek

Bürget

Povoda

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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With growing number of stored image data, image search and image similarity problem become more and more important. The answer can be solved by Content-Based Image Retrieval systems. This paper deals with an image search using similarity measures based on circular sectors method. The method is inspired by human eye functionality. The main contribution of the paper is a modified method that increases accuracy for about 8% in comparison with original approach. Here proposed method has used HSB colour model and median function for feature extraction. The original approach uses RGB colour model with mean function. Implemented method was validated on 10 image categories where overall average precision was 67%.

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Analysis of image similarity with CBIR concept using wavelet transform and threshold algorithm

Cited by 9 publications

References 13 publications

Searching Similarity Digital Image Using Color Histogram

Searching Similarity Digital Image Using Color Histogram

A Refined Approach of Image Retrieval Using RBF-SVM Classifier

Image Search Using Similarity Measures Based on Circular Sectors

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