A Novel Strategy for Retrieving Large Scale Scene Images Based on Emotional Feature Clustering

Abstract

Due to complicated data structure, image can present rich information, and so images are applied widely at different fields. Although the image can offer a lot of convenience, handling such data consume much time and multi-dimensional space. Especially when users need to retrieve some images from larger-scale image datasets, the disadvantage is more obvious. So, in order to retrieve larger-scale image data effectively, a scene images retrieval strategy based on the MapReduce parallel programming model is proposed. The proposed strategy first, investigates how to effectively store large-scale scene images under a Hadoop cluster parallel processing architecture. Second, a distributed feature clustering algorithm MeanShift is introduced to implement the clustering process of emotional feature of scene images. Finally, several experiments are conducted to verify the effectiveness and efficiency of the proposed strategy in terms of different aspects such as retrieval accuracy, speedup ratio and efficiency and data scalability.