Diane Lit manStandard object-oriented languages do not provide language support for modeling changing collections of interdependent objects. We propose that R++, an integration of the rule and objectoriented paradigms, provides a mechanism for easily implementing such models. R++ extends C++ by adding a new programming construct called the path-based rule. Such data-driven rules are restricted to follow pointers between objects, and are like "automatic methods" that are triggered by changes to monitored objects. Path-based rules encourage a more abstract level of programming, and unlike previous rule integrations, are not at odds with the object-oriented paradigm and offer performance advantages for natural applications.
Object-oriented languages and rule-based languages o er two distinct and useful programming abstractions. However, previous attempts to integrate data-driven rules into object-oriented languages have typically achieved an uneasy union at best. R++ is a new, closer integration of the rule-based and object-oriented paradigms that extends C++ with a single programming construct, the path-based rule, as a new kind of class member. Path-based rules|data-driven rules that are restricted to follow pointers between objects|are like \automatic methods" that are triggered by changes to the objects they monitor. Path-based rules provide a useful level of abstraction that encourage a more declarative style of programming, and are valuable in object-oriented designs as a means of modeling dynamic collections of interdependent objects. Unlike more traditional pattern-matching rules, path-based rules are not at odds with the object-oriented paradigm, and o er performance advantages for many natural applications.
Content-Based Image Retrieval (CBIR)allows search and recovery of pictures that are similar to a known picture, by using attributes that represent the visual content of the pictures. Our proposed study aimed to model a method for the recovery of indexed pictures in databases from their visual content, without the need for textual annotations. Gray Level Co-Occurrence Matrix (GLCM), Harris Corner, and Histogram of Oriented Gradients (HOG) attributes are extracted from the publicly available databases (Corel and Caltech Datasets). A novel nonparametric method of texture combination is applied by means of Principal Components Analysis (PCA). Finally the classification is accomplished by using Support Vector Machine (SVM) and Random Forest Classifiers. The simulation outcomes show satisfactory performance accordant with accuracy, recall, precision and F-score.
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