By considering the importance of detecting fish and other aquatic objects, and also by considering the specifications of marine environment such as low quality and brightness of underwater images, light break, high level of noise, the overlapping of the fish object with the plants and the sands, nowadays researchers are looking for suitable methods for separating and segmenting fishes in this category of images. So far, various algorithms have been proposed to separate the fish objects from the underwater images, but these algorithms often faced various challenges such as strong noise in underwater images and superwised-based segmentation algorithms for underwater images which caused to use assumptions as identify the approximate foreground from background region.
In this research, a new method has been introduced for isolating the fish object from underwater images. In this approach, a new color space was created for more accurate separation of pixels and detection of the fish object by combining the components of both color spaces RGB and CIE L×a×b. The new proposed method eliminates the limitation of the famous salient object detection algorithm which necessarily considers an approximate regions of the foreground and background. The performance of the proposed method significantly has been investigated by using the common evaluation criteria including accuracy, recall, precision and F_measure, which these indexes have been increased 5%, 3%, 4% and 1%, respectively, compared to the best rival method.