Learning Method For Image Classification Research Articles

Semi-Supervised Image Classification Based on Local and Global Regression

The insufficiency of labeled samples is a major problem in automatic image annotation. However, unlabeled samples are readily available and abundant. Hence, semi-supervised learning methods, which utilize partly labeled samples and a large amount of unlabeled samples, have attracted increased attention in the field of image classification. During the past decade, graph-based semi-supervised learning became one of the most important research areas in semi-supervised learning. In this letter, we propose a novel and effective graph based semi-supervised learning method for image classification. The new method is based on local and global regression regularization. The local regression regularization adopts a set of local classification functions to preserve both local discriminative and geometrical information; while the global regression regularization preserves the global discriminative information and calculates the projection matrix for out-of-sample extrapolation. Extensive simulations based on synthetic and real-world datasets verify the effectiveness of the proposed method.

An EM based multiple instance learning method for image classification

In this paper, we propose an EM based learning algorithm to provide a comprehensive procedure for maximizing the measurement of diverse density on given multiple Instances. Furthermore, the new EM based learning framework converts an MI problem into a single-instance treatment by using EM to maximize the instance responsibility for the corresponding label of each bag. To learn a desired image class, a user may select a set of exemplar images and label them to be conceptual related (positive) or conceptual unrelated (negative) images. A positive image consists of at least one object that the user may be interested, and a negative image should not contain any object that the user may be interested. By using the proposed EM based learning algorithm, an image retrieval prototype system is implemented. Experimental results show that for only a few times of relearning cycles, the prototype system can retrieve user's favor images from WWW over Internet.