Research on Fast Extraction of Information System from Online Social Network Images Based on Big Data Algorithm

The paper proposes a social network image tag sorting big data algorithm, which combines SIFT features, convolutional neural network features, and visual bag-of-words model to obtain the target image's visual neighbour image set from the image training set. The paper makes all the visual neighbour images the initial label of the target image for weighted voting and calculates the voting weight through the linear fusion of visual image similarity and label semantic similarity. Simultaneously, the paper uses the target image labels and their visual neighbors to construct a label graph model and uses the weighted voting results to perform a random walk on the label graph to complete the label ranking task. The experimental results verify the effectiveness of the proposed method.