Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking. Following the tracking-by-detection framework, an online MILBoost approach is developed that sequentially chooses weak classifiers by maximizing the bag likelihood. In this paper, we extend this idea towards incorporating the instance significance estimation into the online ...

In Multiple Instance Learning (MIL) problems, objects are represented by a set of feature vectors, in contrast to the standard pattern recognition problems, where objects are represented by a single feature vector. Numerous classifiers have been proposed to solve this type of MIL classification problem. Unfortunately only two datasets are standard in this field (MUSK-1 and MUSK-2), and all clas...

We describe a generalization of the multiple-instance learning model in which a bag’s label is not based on a single instance’s proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We list potential applications of this model (robot vision, content-based image retrieval, protein sequence iden...

This paper introduces the use of multi-objective evolutionary algorithms in multiple instance learning. In order to achieve this purpose, a multi-objective grammar-guided genetic programming algorithm (MOG3P-MI) has been designed. This algorithm has been evaluated and compared to other existing multiple instance learning algorithms. Research on the performance of our algorithm is carried out on...

Despite the fact that there is critical grammatical information expressed through facial expressions and head gestures, most research in the field of sign language recognition has primarily focused on the manual component of signing. We propose a novel framework for robust tracking and analysis of non-manual behaviours, with an application to sign language recognition. The novelty of our method...

Multiple Instance Learning is concerned with learning from sets (bags) of feature vectors (instances), where the bags are labeled, but the instances are not. One of the ways to classify bags is using a (dis)similarity space, where each bag is represented by its dissimilarities to certain prototypes, such as bags or instances from the training set. The instance-based representation preserves the...

Metric learning aims at finding a distance that approximates a task-specific notion of semantic similarity. Typically, a Mahalanobis distance is learned from pairs of data labeled as being semantically similar or not. In this paper, we learn such metrics in a weakly supervised setting where “bags” of instances are labeled with “bags” of labels. We formulate the problem as a multiple instance le...

In this paper, we propose a Semi-Supervised MultipleInstance Learning (SSMIL) algorithm, and apply it to Localized ContentBased Image Retrieval(LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comp...

Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problem have been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e., leaving ou...

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