An efficient algorithm for attention-driven image interpretation from segments

In the attention-driven image interpretation process, an image is interpreted as containing several perceptually attended objects as well as the background. The process benefits greatly a content-based image retrieval task with attentively important objects identified and emphasized. An important issue to be addressed in an attention-driven image interpretation is to reconstruct several attentive objects iteratively from the segments of an image by maximizing a global attention function, The object reconstruction is a combinational optimization problem with a complexity of 2(N) which is computationally very expensive when the number of segments N is large. in this paper, we formulate the attention-driven image interpretation process by a matrix representation. An efficient algorithm based on the elementary transformation of matrix is proposed to reduce the Computational complexity to 3 omega N(N - 1)(2)/2, where omega is the number of runs. Experimental results oil both the synthetic and real data show a significantly improved processing speed with air acceptable degradation to the accuracy of object formulation. (C) 2008 Elsevier Ltd. All rights reserved.