Combination of Local Feature Extraction for Image Retrieval

Narayanan, S. Sankara; Vinod, D.; Athisayamani, Suganya; Singh, Anshuman

doi:10.1007/978-981-16-4538-9_32

Cited by 1 publication

(1 citation statement)

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“…The extraction of various features of the image was adequately acknowledged using Local Binary Patterns (LBP). An image retrieval method was proposed using directional image features and an adaptive threshold [20]. Medical images are retrieved using several different distance methods and color difference histograms [21].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Related Workmentioning

confidence: 99%