scholarly journals Sketch-Based Image Retrieval with Histogram of Oriented Gradients and Hierarchical Centroid Methods

2020 ◽  
Vol 188 ◽  
pp. 00026
Author(s):  
Viny Christanti Mawardi ◽  
Yoferen Yoferen ◽  
Stéphane Bressan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Euclidean Distance ◽  
High Threshold ◽  
Histogram Of Oriented Gradients ◽  
Test Results ◽  
Average Precision ◽  
Canny Edge Detection ◽  
Image Dataset ◽  
Canny Edge

Searching images from digital image dataset can be done using sketch-based image retrieval that performs retrieval based on the similarity between dataset images and sketch image input. Preprocessing is done by using Canny Edge Detection to detect edges of dataset images. Feature extraction will be done using Histogram of Oriented Gradients and Hierarchical Centroid on the sketch image and all the preprocessed dataset images. The features distance between sketch image and all dataset images is calculated by Euclidean Distance. Dataset images used in the test consist of 10 classes. The test results show Histogram of Oriented Gradients, Hierarchical Centroid, and combination of both methods with low and high threshold of 0.05 and 0.5 have average precision and recall values of 90.8 % and 13.45 %, 70 % and 10.64 %, 91.4 % and 13.58 %. The average precision and recall values with low and high threshold of 0.01 and 0.1, 0.3 and 0.7 are 87.2 % and 13.19 %, 86.7 % and 12.57 %. Combination of the Histogram of Oriented Gradients and Hierarchical Centroid methods with low and high threshold of 0.05 and 0.5 produce better retrieval results than using the method individually or using other low and high threshold.

scholarly journals Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG

2017 ◽  
Vol 6 (3) ◽  
pp. 271-280
Author(s):  
Cut Mutia ◽  
Fitri Arnia ◽  
Rusdha Muharar
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Euclidean Distance ◽  
Retrieval System ◽  
Content Based Image Retrieval ◽  
Histogram Of Oriented Gradients ◽  
Retrieval Performance ◽  
Islamic Women ◽  
Online Shops ◽  
Shape Feature Extraction

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.

scholarly journals Trafic Signs Detection Based On Saliency Map Using Canny Edge

2018 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Putri Alit Widyastuti Santiary ◽  
I Made Oka Widyantara ◽  
Rukmi Sari Hartati
Keyword(s):  
Edge Detection ◽  
Euclidean Distance ◽  
Exact Result ◽  
Saliency Map ◽  
Traffic Sign ◽  
Canny Edge Detection ◽  
Pixel Intensity ◽  
The Road ◽  
Road Sign ◽  
Canny Edge

This paper proposed Canny edge detection to detected saliency map on traffic sign. The edge detection functions by identifying the bounds from an object on an image. The edge of an image is an area that has a strong intensity of light.The pixel intensity of an image changes from low to high values or otherwise. Detecting the edge of an image significantly will decrease the amount of data and filters insignificant information by not deleting necessary structure from the image. The image used for this paper is a digital capture of a traffic sign with a background. The result of this study shows that Canny edge detection creates saliency map from the traffic sign and separates the road sign from the background. The image result tested by calculating the saliency distance between a tested image and trained image using normalized Euclidean distance. The value ofnormalized Euclidean distance is set between 0 to 2. The testing process is done by calculating the nearest distance between the tested vector features and trained vector features. From the examination as a whole, it can be concluded that road sign detection using saliency map model can be built by Canny edge detection. From the whole system examination, it resulted a accuracy value of 0,65. This value shows that the data was correctly classified by 65%. The precision value has an outcome of 0,64, shows that the exact result of the classification is 64%. The recall value has an outcome of 0,94. This value shows that the success rate of recognizing a data from the whole data is 94%.

Based on Canny Operator with Regional Growth Combined Research on Accurate Image Segmentation Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2088-2092 ◽  
Author(s):  
Jia Qing Miao
Keyword(s):  
Edge Detection ◽  
Regional Growth ◽  
Test Results ◽  
Canny Operator ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Continuous Boundary ◽  
Region Growth ◽  
Image Segmentation Algorithm ◽  
Growing Region

In this paper, first we introduce the digital image algorithm of Canny edge detection, and then simulation results of Canny edge detection are given particularly. Test results show that the improved algorithm improves the accuracy of edge detection effectively. Secondly, the algorithm of the region growth, which algorithm is divided into two parts: one is growing region; the other is merging region, is realized. Finally, based on above analysis, we find out that a continuous boundary is not obtained by edge detection, and the method of regional extraction may produce transition segmentation. In response to these deficiencies, we present a region growth algorithm of edge detection.

scholarly journals IR-HF-WED: IMAGE RETRIEVAL USING HYBRID FEATURE EXTRACTION WITH WEIGHTED EUCLIDEAN DISTANCE

2020 ◽  
Vol 9 (6) ◽  
pp. 3987-3999
Author(s):  
K. Venkataravana Nayak ◽  
M. Geetanjali ◽  
J. S. Arunalatha ◽  
K. R. Venugopal
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Euclidean Distance ◽  
Hybrid Feature Extraction

scholarly journals Content Based Image Retrieval using Feature Extraction Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 2527-2532
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Recall Rate ◽  
Distance Metrics ◽  
Average Precision ◽  
Feature Extraction Method ◽  
Retrieval Technique ◽  
Precision Rate ◽  
Feature Extraction Technique

Now days the image processing can be used in various areas such as in Agriculture, in Health care system also for security purpose. In case of Crime investigation the image processing can be used to identify the particular suspect from an available dataset for that purpose an image retrieval technique is presented in this paper. For image retrieval number of techniques is available. In earlier days Block Truncation Coding is used but due its some disadvantage feature extraction method is used. Using DDBTC technique two features are derived. The first feature as Color Co-occurrence Features (CCF) obtained using color quantizes features such as Bit Pattern Feature (BPF) is derived from Bitmap image. The five different distance metrics are used to measure the similarity between two images. The simulated results shows proposed Technique can shows the better result in the form of Average Precision rate (APR) and Average Recall Rate (ARR) as compared to other techniques.

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