An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram

Himani Chugh, Sheifali Gupta, Meenu Garg, Deepali Gupta, Heba G. Mohamed, Irene Delgado Noya, Aman Singh and Nitin Goyal ()
Additional contact information
Himani Chugh: Chandigarh Group of Colleges, Landran, Mohali 140307, Punjab, India
Sheifali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Meenu Garg: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Deepali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Heba G. Mohamed: Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Irene Delgado Noya: Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
Aman Singh: Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
Nitin Goyal: Computer Science and Engineering Department, Shri Vishwakarma Skill University, Palwal 121102, Haryana, India

Sustainability, 2022, vol. 14, issue 16, 1-15

Abstract: This paper focuses on retrieving plant leaf images based on different features that can be useful in the plant industry. Various images and their features can be used to identify the type of leaf and its disease. For this purpose, a well-organized computer-assisted plant image retrieval approach is required that can use a hybrid combination of the color and shape attributes of leaf images for plant disease identification and botanical gardening in the agriculture sector. In this research work, an innovative framework is proposed for the retrieval of leaf images that uses a hybrid combination of color and shape features to improve retrieval accuracy. For the color features, the Color Difference Histograms (CDH) descriptor is used while shape features are determined using the Saliency Structure Histogram (SSH) descriptor. To extract the various properties of leaves, Hue and Saturation Value (HSV) color space features and First Order Statistical Features (FOSF) features are computed in CDH and SSH descriptors, respectively. After that, the HSV and FOSF features of leaf images are concatenated. The concatenated features of database images are compared with the query image in terms of the Euclidean distance and a threshold value of Euclidean distance is taken for retrieval of images. The best results are obtained at the threshold value of 80% of the maximum Euclidean distance. The system’s effectiveness is also evaluated with different performance metrics like precision, recall, and F-measure, and their values come out to be respectively 1.00, 0.96, and 0.97, which is better than individual feature descriptors.

Keywords: color difference histogram; saliency structure histogram; HSV; FOSF (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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