eprintid: 5633 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/56/33 datestamp: 2023-01-31 23:30:11 lastmod: 2023-07-11 23:30:34 status_changed: 2023-01-31 23:30:11 type: article metadata_visibility: show creators_name: Chugh, Himani creators_name: Gupta, Sheifali creators_name: Garg, Meenu creators_name: Gupta, Deepali creators_name: Mohamed, Heba G. creators_name: Delgado Noya, Irene creators_name: Singh, Aman creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: irene.delgado@uneatlantico.es creators_id: aman.singh@uneatlantico.es creators_id: title: An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: color difference histogram; saliency structure histogram; HSV; FOSF 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. date: 2022-08 publication: Sustainability volume: 14 number: 16 pagerange: 10357 id_number: doi:10.3390/su141610357 refereed: TRUE issn: 2071-1050 official_url: http://doi.org/10.3390/su141610357 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Chugh, Himani; Gupta, Sheifali; Garg, Meenu; Gupta, Deepali; Mohamed, Heba G.; Delgado Noya, Irene; Singh, Aman y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, aman.singh@uneatlantico.es, SIN ESPECIFICAR (2022) An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram. Sustainability, 14 (16). p. 10357. ISSN 2071-1050 document_url: http://repositorio.unic.co.ao/id/eprint/5633/1/electronics-11-02637-v2.pdf