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The image retrieval and semantic extraction play an important role in the multimedia systems such as geographic information system, hospital information system, digital library system, etc. Therefore, the research and development of semantic-based image retrieval (SBIR) systems have become extremely important and urgent. Major recent publications are included covering different aspects of the research in this area, including building data models, low-level image feature extraction, and deriving high-level semantic features. However, there is still no general approach for semantic-based image retrieval (SBIR), due to the diversity and complexity of high-level semantics. In order to improve the retrieval accuracy of SBIR systems, our focus research is to build a data structure for finding similar images, from that retrieving its semantic. In this paper, we proposed a data structure which is a self-balanced clustering tree named C-Tree.  Firstly, a method of visual semantic analysis relied on visual features and image content is proposed on C-Tree. The building of this structure is created based on a combination of methods including hierarchical clustering and partitional clustering. Secondly, we design ontology for the image dataset and create the SPARQL (SPARQL Protocol and RDF Query Language) query by extracting semantics of image. Finally, the semantic-based image retrieval on C-Tree (SBIR\_CT) model is created hinging on our proposal. The experimental evaluation 20,000 images of ImageCLEF dataset indicates the effectiveness of the proposed method. These results are compared with some of recently published methods on the same dataset and demonstrate that the proposed method improves the retrieval accuracy and efficiency.

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A LY THUYET DIEM BAT DONG TRONG CO SO nULI~U suv nAN PTS. LE MANH THACH I Tom tat nQi dung. BAi bao dua tren eo sa ly~huyet.di~m bat dong cua mot chuyen d!.cht~en mot, dun dily du d~xac l~p ngii' nghia thich hop cho chirong trinh Datalog va cac twang hop ma rong cua no: trong truong hop Datalog ngir nghia la mo hinh nho nhat (minimal model), trong tnrong hop Datalog morong eo phu dinh xep tang thi ngir nghia duce xac I~p la mot mo hinh hoan thi~n (perfect model), con trong tnrong hop phu dinh tong quat thl la mo hlnh lam phat (inflationarY model). I. MOT s6 KHAI NI~M CO BAN. Trong khuon kho bai bao toi xin phep khong nhac lai 0 day mot so khai niem trong ngon ngU'logic bac nhat nhir term, atom, literal, fact, ... (doe gia eo the xem 6 [Cl D. Chuang trinh logic: Chirong trinh logic la tap hop hiru han, khac rong P cac cong thirc bac nhat dang: SI 1\ S2'" 1\ Sk~P (k~0), trong do p la mot atom, Si (i = 1, ...k) la cac literal, 1\ ky hieu ghep hoi logic,~ky hieu phep keo hteo logic.

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