Object Detection using Deep Learning with Hierarchical Multi Swarm Optimization

Till now there is a huge research had in the field of visual information retrieval, but with the growth of data and with less processing speed we are not meeting the needs of current problem. The main focus of this paper is to identify the objects with salient features and object highlighting. Till now object identification is done with the pixel based or with the region based. Different methodologies are compared in this work and they will be processed with the learning work. Multi scale contrast is one of the pixel based technology where object borders are identified but not the object. This can be done with the histogram contrast. Still it is not covering all the features of the object and it is not clear in identifying the objects at high contrast regions. To solve this issue region based contrasting method is used which is the better solution for all this object identification. After extracting the features and identifying the object, now auto classification or identification of the object should be done. The other part of the work mainly concentrates on the learning system which uses most popular neural network algorithms. Identifying the drawbacks of neural network algorithms and proposing the new methodology identify the objects is done in this paper