engleski

Multi-Level Image Annotation Using Bayes Classifier and Fuzzy Knowledge Representation Scheme

Automatic image annotation (AIA) is the process by which metadata, in form of keywords or text descriptions are automatically assigned to an unlabeled image. Generally, two problems can be distinguished: the problem of semantic extraction, due to the gap between the image features and object labels, and the problem of semantic interpretation, due to the gap between the object labels and the human interpretation of images. In this paper, a model for multi-level image annotation that is performed in two phases is proposed. In the first phase, a Naïve Bayes classifier is used to classify low-level image features into elementary classes. In the second phase, a knowledge representation scheme based on Fuzzy Petri Net is used to expand the level of vocabulary and to include multi-level semantic concepts related to images into image annotations. In the paper, a knowledge representation scheme for outdoor image annotation is given. Procedures for determining concepts related to an image using fuzzy recognition and inheritance algorithms on a knowledge representation scheme are presented, as well as experimental results of image annotation.

Multi-level Image Annotation ; Fuzzy Petri Net ; Knowledge Representation ; Naïve Bayes

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