Managing complex taxonomic data in an object-oriented database.

This thesis addresses the problem of multiple overlapping classifications in object-oriented databases through the example of plant taxonomy. These multiple overlapping classifications are independent simple classifications that share information (nodes and leaves), therefore overlap. Plant taxonomy was chosen as the motivational application domain because taxonomic classifications are especially complex and have changed over long periods of time, therefore overlap in a significant manner. This work extracts basic requirements for the support of multiple overlapping classifications in general, and in the context of plant taxonomy in particular. These requirements form the basis on which a prototype is defmed and built. The prototype, an extended object-oriented database, is extended from an object-oriented model based on ODMG through the provision of a relationship management mechanism. These relationships form the main feature used to build classifications. This emphasis on relationships allows the description of classifications orthogonal to the classified data (for reuse and integration of the mechanism with existing databases and for classification of non co-operating data), and allows an easier and more powerful management of semantic data (both within and without a classification). Additional mechanisms such as integrity constraints are investigated and implemented. Finally, the implementation of the prototype is presented and is evaluated, from the point of view of both usability and expressiveness (using plant taxonomy as an application), and its performance as a database system. This evaluation shows that the prototype meets the needs of taxonomists

Analysis of produce recognition system with taxonomist's knowledge using computer vision and different classifiers

Supermarkets nowadays are equipped with barcode scanners to speed up the checkout process. Nevertheless, most of the agricultural products cannot be pre-packaged and thus must be weighted. The development of produce recognition system based on computer vision could help the cashiers in the supermarkets with the pricing of these weighted products. This work proposes a hybrid approach of object classification and attribute classification for the produce recognition system which involves the cooperation and integration of statistical approaches and semantic models. The integration of attribute learning into the produce recognition system was proposed due to the fact that attribute learning has emerged as a promising paradigm for bridging the semantic gap and assisting in object recognition in many fields of study. This could tackle problems occurred when less training data are available, i.e. less than 10 samples per class. The experiments show that the correct classification rate of the hybrid approach were 60.55, 75.37 and 86.42% with 2, 4 and 8 training examples, respectively, which were higher than other individual classifiers. A well-balanced specificity, sensitivity and F1 score were achieved by the hybrid approach for each produce type