Object recognition using a cognitive swarm vision framework with attention mechanisms

A computer program product for object recognition, the computer program product comprising computer-readable instruction means encoded on a computer-readable medium for causing a computer to, when executed, perform the operations of:

• configuring at least one cognitive map to have a one-to-one relationship with an input image domain, the cognitive map being capable of recording information that software agents utilize to focus a cooperative swarm's attention on regions in the domain most likely to contain objects of interest;
• configuring a plurality of software agents 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;
• and
• further comprising instruction means for causing a computer to:
• running and maintaining a sorted list for gbest and pbest, along with the associated FA values, where FA is an objective function and is calculated according to the following:
• FA=μ(Q+−Q−)+(1−μ)FC,
• where Q+ denotes an attracting pheromone and Q− denotes a repelling pheromone, and where m is a nonnegative weighting factor, and FC is an object classifier confidence value;
• and
• updating the cognitive map at each iteration of the swarm and update FA for each entry in the sorted list, whereby the swarm is modified by the cognitive map in such a way as to focus attention on regions of increased saliency.

A computer implemented method for object recognition using swarming domain classifiers, when executed on the computer comprises the acts of:

• configuring at least one cognitive map to have a one-to-one relationship with an input image domain, the cognitive map being capable of recording information that software agents utilize to focus a cooperative swarm's attention on regions in the domain most likely to contain objects of interest;
• configuring a plurality of software agents 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;
• and
• running and maintaining a sorted list for gbest and pbest, along with the associated FA values, where FA is an objective function and is calculated according to the following:
• FA=μ(Q+−Q−)+(1−μ)FC,
• where Q+ denotes an attracting pheromone and Q− denotes a repelling pheromone, and where m is a nonnegative weighting factor, and FC is an object classifier confidence value;
• and
• updating the cognitive map at each iteration of the swarm and updating FA for each entry in the sorted list, whereby the swarm is modified by the cognitive map in such a way as to focus attention on regions of increased saliency.

A computer program product for object recognition, the computer program product comprising computer-readable instruction means encoded on a computer-readable medium for causing a computer to, when executed, perform the operations of:

• receiving a first current input image;
• initializing a global swarm to search for objects within the input image;
• assigning local swarms to objects identified by the global swarm;
• receiving a next input image, where the next input image is deemed the current input image and a previous current input image is deemed the previous input image;
• initializing the local swarms to search for and identify objects in the current input image;
• deleting local swarms that lost their identified objects between the current and previous input images;
• initializing the global swarm to search for new objects in the current input image;
• assigning local swarms to new objects identified in the current input image;
• and
• repeating the operations of receiving a next input image, initializing, deleting, initializing, and assigning for subsequent next images;
• and
• further camp rising instruction means for causing a computer to:
• running and maintaining a sorted list for gbest and pbest, along with the associated FA values, where FA is an objective function and is calculated according to the following:
• FA=μ(Q+−Q−)+(1−μ)FC,
• where Q+ denotes an attracting pheromone and Q− denotes a repelling pheromone, and where m is a nonnegative weighting factor, and FC is an object classifier confidence value;
• and
• updating a cognitive map at each iteration of the swarm and update FA for each entry in the sorted list, whereby the swarm is modified by the cognitive map in such a way as to focus attention on regions of increased saliency.