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While image captioning through machines requires structured learning andbasis for interpretation, improvement requires multiple context understandingand processing in a meaningful way. This research will provide a novel conceptfor context combination and will impact many applications to deal visualfeatures as an equivalence of descriptions of objects, activities and events.There are three components of our architecture: Feature DistributionComposition (FDC) Layer Attention, Multiple Role Representation Crossover(MRRC) Attention Layer and the Language Decoder. FDC Layer Attention helps ingenerating the weighted attention from RCNN features, MRRC Attention Layer actsas intermediate representation processing and helps in generating the next wordattention, while Language Decoder helps in estimation of the likelihood for thenext probable word in the sentence. We demonstrated effectiveness of FDC, MRRC,regional object feature attention and reinforcement learning for effectivelearning to generate better captions from images. The performance of our modelenhanced previous performances by 35.3\% and created a new standard and theoryfor representation generation based on logic, better interpretability andcontexts.

MRRC: Multiple Role Representation Crossover Interpretation for Image Captioning With R-CNN Feature Distribution Composition (FDC)