Multi-feature query language for image classification

Raoul Pascal Pein, Joan Lu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Despite the major effort put into the creation of Content-Based Image Retrieval (CBIR) systems during the last decade, the solutions available are still not satisfying for generic purposes. The most severe issue seems to be the so-called "semantic gap". It is feasible to define and use domain specific feature vectors on a low level and use this information for a similarity based retrieval. Yet, mapping these to higher level semantics remains difficult. This research investigates a domain-independent way of automatized image categorization. A CBIR query language is constructed to build query-like descriptors for each category to be learned. The proposed learning algorithm is based on decision-trees. The resulting descriptors are aimed to be understandable and modifiable by expert users. A case-study is presented to support these claims.

Original languageEnglish
Pages (from-to)2549-2557
Number of pages9
JournalProcedia Computer Science
Issue number1
Publication statusPublished - 1 May 2010


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