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Since the labelling for the positive images/videos is ambiguous in weaklysupervised segment annotation, negative mining based methods that only use theintra-class information emerge. In these methods, negative instances areutilized to penalize unknown instances to rank their likelihood of being anobject, which can be considered as a voting in terms of similarity. However,these methods 1) ignore the information contained in positive bags, 2) onlyrank the likelihood but cannot generate an explicit decision function. In thispaper, we propose a voting scheme involving not only the definite negativeinstances but also the ambiguous positive instances to make use of the extrauseful information in the weakly labelled positive bags. In the scheme, eachinstance votes for its label with a magnitude arising from the similarity, andthe ambiguous positive instances are assigned soft labels that are iterativelyupdated during the voting. It overcomes the limitations of voting using onlythe negative bags. We also propose an expectation kernel density estimation(eKDE) algorithm to gain further insight into the voting mechanism.Experimental results demonstrate the superiority of our scheme beyond thebaselines.

Weakly supervised segment annotation via expectation kernel density estimation