1. An object recognition system incorporating swarming domain classifiers, comprising: at least one cognitive map stored in memory having a one-to-one relationship with an input image domain, the cognitive map being capable of recording information in the memory that software agents utilize to focus a cooperative swarm's attention on regions in the domain most likely to contain objects of interest; a plurality of software agents executing on a processor configured to operate as a cooperative swarm to classify an object in the domain, where each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions, where each agent is configured to perform at least one iteration as influenced by the recorded information of the cognitive map, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest), where the gbest is used to store the best location among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold; the cognitive map is a map selected from a group consisting of a ground surface map, an interest map, an object map, and a saliency map; and the interest map stored in the memory is configured to run on the processor and maintain a sorted list for gbest and pbest, along with the associated F A values, where F A is an objective function and is calculated according to the following: 
 F A =μ( Q + −Q − )+(1−μ) F C , where Q + denotes an attracting pheromone and Q − denotes a repelling pheromone, and where m is a nonnegative weighting factor, and F C is an object classifier confidence value; and the interest map is updated in the memory at each iteration of the swarm and F A is updated for each entry in the sorted list, whereby the swarm is modified by the interest map in such a way as to focus attention on regions of increased saliency.