Rank-Two Relaxation Heuristics for MAX-CUT and Other Binary Quadratic Programs
SIAM Journal on Optimization
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Semantic Annotation of Real-World Web Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A novel graph-based approach to automatically refine image annotation is presented in this paper. Given an unannotated image, a set of candidate annotations is extracted by the existing image annotation method. Then, each candidate annotation is converted to vertex of a graph and the semantic similarity between two candidate annotations is used as edge weight. Next, a heuristics graph algorithm solving weighted MAX-CUT problem is used to prune the noisy annotations. Experimental results demonstrate the effectiveness of our image annotation refinement algorithm.