An Automatic Object Retrieval Framework for Complex Background

In this paper we propose a novel framework for object retrieval based on automatic foreground object extraction and multi-layer information integration. Specifically, user interested objects are firstly detected from unconstrained videos via a multimodal cues method, then an automatic object extraction algorithm based on Grab Cut is applied to separate foreground object from background. The object-level information is enhanced during the feature extraction layer by assigning different weights to foreground and background pixels respectively, and the spatial color and texture information is integrated during the similarity calculation layer. Experimental results on both benchmark data set and real-world data set demonstrate the effectiveness of the proposed framework.