Shape Retrieval And Automatic Clustering Method Based On Graph Shape Context

In recent years, with the development of Mobile Internet Device and Social Networking Services, people prefer to communicate with each other by using image rather than by using textual words. Billions of image information are sent to the Internet every day with the application of Weibo, Macro-channel, Facebook and Twitter et al. It is difficult for the internet company to find the information of user needed quickly and accurately.In traditional technique, it is time-consuming and inefficient to add the word information on images by hand. So more and more researchers pay attention to recognize and label the image by computer automatically. The content-based image retrieval is the technology of image matching and recognition by applying Mathematical Modeling to abstract the feature of color, shape, texture and space relationship. The feature of shape become a research hotspot since it can describe the senior visual features of image target.Today is a big data time, It will consume mass of computer processor and memory resource by using traditional retrieval measures which searching the corresponding image on the whole Internet. This is really an era that requires stresses efficiency and speed. Technologies of data mining can help users find the useful information quickly and efficiently. The similar images will be parted into one cluster by applying the clustering method, so we just search the corresponding image cluster on the process of image retrieval. In order to deal with the above problems, three aspects are worked in our paper:1. A method of shape retrieval based on graph shape context is proposed. Firstly, a new shape graph structure is constructed by sampling the shape key points by the constrained Delauney triangle principle and building the skeleton graph by these key points; Then, the graph graph is described by shape context to abstract its feature;Finally, the corresponding graph nodes are matched and the similarity distance between pair of shape is calculated by dynamic programming. The experiments prove that our method has a perfect retrieval accuracy, at the same time, our method can reduce the complexity of shape descriptor and the run time of retrieval process.2. A method of shape retrieval based on the local constrained diffusion process is proposed. In the traditional retrieval technology, the shortest distance between pair of image is searched. In our method, firstly, the whole images are regarded as nodes of a graph; Then Markov Probabilistic Model is adopted to diffuse the similarity information between shapes to the whole data; Finally, local constrained method is employed to erase the negative effects of noisy image. In our experiments, this training method can be verified to enhance the performance of our retrieval results effectively.3. A method of shape automatic clustering based multi-objective optimization with decomposition approach is proposed. In this method, The muliti-objective optimization is introduced into the the area of shape clustering. Firstly, the shape clustering problem is transformed into graph-based partition; Then, two objective functions which have opposite clustered preference are applied and optimized to partition the graph. In recent years, the multi-objective optimization with decomposition(MOEA/D) have a excellent performance in optimal area. So, MOEA/D is adopted as a framework to complete the process of shape clustering in our method.This paper was supported by the National Natural Science Foundation(No.61373111),the Provincial Natural Science Foundation of Shaanxi of China(No. 2014JM8321) and the Fundamental Research Funds for the Central Universities(Nos. K50511020014,K5051302084).