Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14310
Title: Recognition of attentive objects with a concept association network for image annotation
Authors: Fu, H
Chi, Z 
Feng, D
Keywords: Attentive objects
Concept association network (CAN)
Image annotation
Neural network
Visual classifier
Issue Date: 2010
Publisher: Elsevier
Source: Pattern recognition, 2010, v. 43, no. 10, p. 3539-3547 How to cite?
Journal: Pattern recognition 
Abstract: With the advancement of imaging techniques and IT technologies, image retrieval has become a bottle neck. The key for efficient and effective image retrieval is by a text-based approach in which automatic image annotation is a critical task. As an important issue, the metadata of the annotation, i.e., the basic unit of an image to be labeled, has not been fully studied. A habitual way is to label the segments which are produced by a segmentation algorithm. However, after a segmentation process an object has often been broken into pieces, which not only produces noise for annotation but also increases the complexity of the model. We adopt an attention-driven image interpretation method to extract attentive objects from an over-segmented image and use the attentive objects for annotation. By such doing, the basic unit of annotation has been upgraded from segments to attentive objects. Visual classifiers are trained and a concept association network (CAN) is constructed for object recognition. A CAN consists of a number of concept nodes in which each node is a trained neural network (visual classifier) to recognize a single object. The nodes are connected through their correlation links forming a network. Given that an image contains several unknown attentive objects, all the nodes in CAN generate their own responses which propagate to other nodes through the network simultaneously. For a combination of nodes under investigation, these loopy propagations can be characterized by a linear system. The response of a combination of nodes can be obtained by solving the linear system. Therefore, the annotation problem is converted into finding out the node combination with the maximum response. Annotation experiments show a better accuracy of attentive objects over segments and that the concept association network improves annotation performance.
URI: http://hdl.handle.net/10397/14310
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2010.04.009
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