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Pattern recognition is an important technique in image processing which is used to identify a particular portion from an image based on various optimization algorithmic extraction methods. This extraction phase includes detecting, matching, converging and grouping which is based on input data and its key features. Here we are using artificial fish swarm algorithm (AFSA) for optimizing the required output, because it is a best global optimization technique. Based on observations in real fish: prey, follow and swarm process, here a virtual fish swarm process is designed and it is applied to the process of extraction. By means of adjusting the other parameters and keeping the constants such as visual and step we will achieve the desired optimized output. A convex optimization theory also included along with this for ideal optimization.

A Novel Fish Swarm Optimization Algorithm using Convex Optimization for Pattern Recognition Systems