In this paper, we shall discuss an extension of MTDM model for image retrieval based on user's specification. A MTDM model is composed of objects which describes the static and the matching cont,rol methods for recognition targets. Characteristics of the describing targets are declared by abst,ract members of the MTDM model objects. In t,his way, the differences of data description can be casily hidden. In our proposed image retrieval system, user specifies a MTDM model to each keyword for image retrieval that may be not exist in the image database. The retrieval acquisition is structured and represented in the form of MTDM model. When a keyword is not found in the image database, the relat,ed reasoning model(MTDM model) will be fired to match corresponding objects form each image. By the extension of MTDM model, the differences of dat,a are integrated. The image retrieval system can hat objects in a general way and the recognition model and the retrieving model can be integrated by the MTDM model.

In this paper we propose a new framework of object-oriented model named MTDM (Matching Tree Driving Model) for drawing understanding and verify its ability of noise absorption. MTDM makes use of descriptions of object-oriented style and is an integration of static and dynamic description of recognition target. Static descriptions are for representation of abstract features so that description of structure and restriction become easier. At the same time static descriptions can be independent of matching procedures of recognition target. The dynamic descriptions are for matching control of recognition target in the form of tree structure named matching tree. Matching procedures for complex targets can be easily described with multiple matching trees. By application to several typical engineering drawings, particularly drawings with noises and distortions MTDM is proven to be suitable for multipurpose and multitarget platform

We propose a new way of constructing a drawing recognition system. The system has the ability of rule acquisition and therefore can be easily adopted to drawings of different specifications. Rule acquisition is realized through an empirical learning module, which constructs decision trees from teacher examples and translates the decision trees into production rules for actual recognition. The teacher examples are stored along with the corresponding environmental parameters so that future modification/expansion becomes much more easier. Since most of the attribute values are continuous-valued so that the construction of decision tree is more time consuming, we propose can improved algorithm for more efficient selection of cut points. Experimental results show that the proposed algorithm for cut point selection in decision tree generation improves the efficiency by up to 6 times while ensuring the optimal result.

An object-oriented drawing understanding system named OO-Mudams, which can flexibly adapt to various types of drawings by defining their model, is proposed. OO-Mudams is a model-driven system, with reasoning engine, management of internal database for reasoning results, and model definition for users. By defining the models, the system can be customized to process various kinds of drawings including engineering drawings and layout drawings. In order to manage huge reasoning result space effectively, the system uses an efficient multidimensional data structure named BD-tree, and, to facilitate the reasoning process, the TMS (truth maintenance system) is used to maintain the dependency among reasoning results. OO-Mudams is characterized by its flexible object-oriented system structure and an object-oriented language for defining the model of target objects.<>