scholarly journals A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

2016 ◽  
Vol 57 ◽  
pp. 02002
Author(s):  
Jaspreet Singh Dhanoa ◽  
Anupam Garg
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Shape Feature ◽  
Novel Technique ◽  
Shape Feature Extraction

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 A novel content based image retrieval system using K-means/KNN with feature extraction

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Color Analysis ◽  
Image Retrieval System ◽  
First Time ◽  
System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

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