This paper presents a scheme for feature extraction that can be applied for classification of
corals in submarine coral reef images. In coral reef image classification, texture features are …

Texture analysis is widely performed in the current time as it is considered as an intimate
property of the surface. It is widely used in the field of image processing, remote sensing …

The recognition of coral species based on underwater texture images poses a significant
difficulty for machine learning algorithms, due to the three following challenges embedded in …

Coral reefs are one of the most biodiverse and productive ecosystems on earth, which acts
as a habitat and provides food to a wide range of organisms. The coral reefs protect the …

Local binary pattern is one of the most known descriptors, which is used for texture
classification. Although completed local binary pattern is seemingly the most precise variant …

The performance of local binary pattern (LBP) and many LBP-based variants is usually
limited by rotation, illumination, scale, viewpoint, and the number of training samples. In view …

Coral reef image classification with the help of its texture features is a challenging task, due
to its variation in class samples. This is achieved with the proposed feature descriptor …

Coral reefs are a precious ecosystem that supports majority of marine life. The identification
of coral species is essential in the conservation and monitoring process. Distinguishing the …

In this paper, a novel feature descriptor named Z with Tilted Z Local Binary Pattern (Z⊕
TZLBP) is proposed for extracting coral reef image features efficiently. The aim is to reduce …

Coral reefs, of all marine ecosystems, are the most diverse. They teem with life with almost a
quarter of all ocean species depending upon reefs for food and shelter. Because of the …