An efficient regular expression inference approach for relevant image extraction

• Published in: Applied soft computing Vol. 135; p. 110030
• Main Authors: Agun, Hayri Volkan, Uzun, Erdinç
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2023
• Subjects: Feature extraction; Regular expression inference; Text classification; Web image extraction; Feature extraction; Regular expression inference; Web image extraction; Text classification
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110030

Traditional approaches for extracting relevant images automatically from web pages are error-prone and time-consuming. To improve this task, operations such as preparing a larger dataset and finding new features are used in the web data extraction approaches. However, these operations are difficult and laborious. In this study, we propose a fully-automated approach based on alignment of regular expressions to automatically extract the relevant images from web pages. The automatically constructed regular expressions has been applied to a classification task for the first time. In this respect, a multi-stage inference approach is developed for generating regular expressions from the attribute values of relevant and irrelevant image elements in web pages. The proposed approach reduces the complexity of the alignment of two regular expressions by applying a constraint on a version of the Levenshtein distance algorithm. The classification accuracy of regular expression approaches is compared with the naive Bayes, logistic regression, J48, and multilayer perceptron classifiers on a balanced relevant image retrieval dataset consisting of 360 image element samples for 10 shopping websites. According to the cross-validation results, the regular expression inference-based classification achieved a 0.98 f-measure with only 5 frequent n-grams, and it outperformed other classifiers on the same set of features. The classification efficiency of the proposed approach is measured at 0.108 ms, which is very competitive with other classifiers.
•We propose a regular expression inference approach to extract relevant images.•The approach focuses only on the textual data of HTML elements.•The approach uses a modified version of the Levenshtein distance algorithm.•The approach improves the accuracy and time efficiency of classical classifiers.