The methods based on pattern recognition achieve quite different accuracy when using different features or classifiers. The purpose of research is to construct the flower category recognition method rapidly

measure its performance

reduce the development workload of researchers and improve efficiency. According to the normal procedure of flower category recognition using pattern recognition technology

the steps of the algorithm are programmed as different kinds of processors to build up an expandable system using the plug-in technology

and the researchers can change the image processing and recognition algorithm by choosing the corresponding processors. Based on this

the algorithm is constructed by linking the processor in the way of data flow and represented as a network. We use the XML description file to describe the dependencies of a plugin and object pool to provide the communication foundation between processors which is the base of the scalable construction methods. The processors are encapsulated into processor plugins and managed by the main framework. The processors are divided into three kinds of processors to input data

process data

and buffer data. The core processor plugin controls the execution order of processors and make sure the data inputted to the processor valid. The constructed processor network of flowers category recognition algorithm normally contains input-processor

feature extractors

buffer processor and classifier processor. The input-processor can generate one input data and data index each time. Feature extractors will be run for several times to process the input data sequence. In the procedure of classifier training or accuracy statistic

a feature vector buffer is needed. While the network that represents the algorithm is processing

the ports in processor can transport messages and data produced. Finally we compared and analyzed different features in color

shape and texture using K-Nearest Neighbor and support vector machine classifier respectively. We use HSV color space histogram to reduce the effect of light

Hu moment and edge curvature histogram to represent shape and polar gray-level co-occurrence matrix for texture. Then different accuracies are achieved by changed the parameter. We can use the constructed method to optimize the parameter of each flower feature extractor rapidly. After that

we combine the features which are extracted by flower extractor processors using the optimized parameter as a flower feature vector. At the same time

multithread technology is used to speed up the process of the algorithm constructed. We construct and optimize the algorithm using the method proposed in the paper. The recognition rate is 91.26% for first hypothesis and 98.41% for fifth hypothesis while using the optimized algorithm on a dataset of 68 flower species. We use the proposed method to construct a flower feature extraction algorithm on 853 training images and finish the whole procedure in 2125 s. The connection method of workflow can construct an algorithm fast and benefit the rapid assessment of the performance of different features and classifiers in flowers category recognition. It is applicable to the research and optimization of algorithms. The proposed method based on workflow and plugin technology is easy to use and flexible and the algorithm constructed has good scalability. Furthermore

the system can be applied to the other researches using the methods of pattern recognition based on digital image.