Feature weighting and retrieval methods for dynamic texture motion features

Feature weighing methods are commonly used to find the relative significance among a set of features that are effectively used by the retrieval methods to search image sequences efficiently from large databases. As evidenced in the current literature, dynamic textures (image sequences with regular motion patterns) can be effectively modelled by a set of spatial and temporal motion distribution features like motion co-occurrence matrix. The aim of this paper is to develop effective feature weighting and retrieval methods for a set of dynamic textures while characterized by motion co-occurrence matrices

Application of Machine Learning Techniques in Aquaculture

ABSTRACT: In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications

Feature weighting and retrieval methods for dynamic texture motion features

Feature weighing methods are commonly used to find the relative significance among a set of features that are effectively used by the retrieval methods to search image sequences efficiently from large databases. As evidenced in the current literature, dynamic textures (image sequences with regular motion patterns) can be effectively modelled by a set of spatial and temporal motion distribution features like motion co-occurrence matrix. The aim of this paper is to develop effective feature weighting and retrieval methods for a set of dynamic textures while characterized by motion co-occurrence matrices