Abstract. In content-based image retrieval (CBIR) and image screening, it is often desirable to locate the regions of interest (ROI) in the images automatically. This can be accomplished with multi-instance learning techniques by treating each image as a bag of instances (regions). Many SVM-based methods are successful in predicting the bag labels, however, few of them can locate the ROIs. Moreover, they are often based on either local search or an EM-style strategy, and may get stuck in local minima easily. In this paper, we propose two convex optimization methods which maximize the margin of concepts via key instance generation at the instance-level and bag-level, respectively. Our formulation can be solved efficiently with a cutting plane algorithm. Experiments show that the proposed methods can effectively locate ROIs, and they also achieve performances competitive with state-of-the-art algorithms on benchmark data sets. 1

Multi-instance (MI) learning is a variant of inductive machine learning where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many multi-instance learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain, this “standard MI assumption ” is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.

We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets. 1.

Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are independently and identically distributed. But there often exists rich additional dependency/structure information between instances/bags within many applications of MIL. Ignoring this structure information limits the performance of existing MIL algorithms. This paper explores the research problem as multiple instance learning on structured data (MILSD) and formulates a novel framework that considers additional structure information. In particular, an effective and efficient optimization algorithm has been proposed to solve the original non-convex optimization problem by using a combination of Concave-Convex Constraint Programming (CCCP) method and an adapted Cutting Plane method, which deals with two sets of constraints caused by learning on instances within individual bags and learning on structured data. Our method has the nice convergence property, with specified precision on each set of constraints. Experimental results on three different applications, i.e., webpage classification, market targeting, and protein fold identification, clearly demonstrate the advantages of the proposed method over state-of-the-art methods. 1

Kernel method is a powerful tool in multi-instance learning. However, many typical kernel methods for multi-instance learning ignore the correspondence information of instances between two bags or co-occurrence information, and result in poor performance. Additionally, most current multiinstance kernels unreasonably assign all instances in each bag an equal weight, which neglects the significance of some “key ” instances in multi-instance learning. Last but not least, almost all the multi-instance kernels encounter a heavy computation load, which may fail in large datasets. To cope with these shortcomings, we propose a FAst kernel for MultiinstancE leaRning named as FAMER. FAMER constructs a Locally Sensitive Hashing (LSH) based similarity measure for multi-instance framework, and represents each bag as a histogram by embedding instances within the bag into an auxiliary space, which captures the correspondence information between two bags. By designing a bin-dependent weighting scheme, we not only impose different weights on instances according to their discriminative powers, but also exploit co-occurrence relations according to the joint statistics of instances. Without directly computing in a pairwise manner, the time complexity of FAMER is much smaller compared to other typical multi-instance kernels. The experiments demonstrate the effectiveness and efficiency of the proposed method. 1