Completing tags by local learning: a novel image tag completion method based on neighborhood tag vector predictor. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Yang, Xiaohong; Yang, Feng
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2407
• Page End: 2416
• Publication Date: 2016-11
• Subjects: Machine learning -- Image tag completion -- Nearest neighbors -- Local linear predictor; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1983-z
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract In this paper, we study the problem of tag completion. Given an image and a set of tags, only a few of the tags are known to be associated with this image or not, and the problem is to predict whether the other tags are associated with the image. To solve this problem, we propose to learn a tag scoring vector for each image and use it to predict the associated tags of the image. To learn the tag scoring vector, we use the method of local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parameters. In this objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.