Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Computational Emotion Analysis From Images: Recent Advances and Future Directions

Emotions are usually evoked in humans by images. Recently, extensive research efforts have been dedicated to understanding the emotions of images. In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective with the focus on summarizing recent advances and suggesting future directions. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve and provide supervised frameworks that are generally used for different IEA tasks. After the introduction of major challenges in IEA, we present some representative methods on emotion feature extraction, supervised classifier learning, and domain adaptation. Furthermore, we introduce available datasets for evaluation and summarize some main results. Finally, we discuss some open questions and future directions that researchers can pursue.Comment: Accepted chapter in the book "Human Perception of Visual Information Psychological and Computational Perspective

Horror image recognition based on context-aware multi-instance learning

Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the Fuzzy Support Vector Machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large scale image sets collected from the Internet

Multimedia and Decision-Making Process

Multimedia technology has changed the way we use computers. Multimedia transforms com-puters into a second person. Multimedia technology has made it possible for us to see, hear, read, feel, and talk to computers. Multimedia technology has transformed our use and understanding of computers. On the other hand, multimedia presentation is one of the fastest-growing sectors of the computer industry. Applications have appeared in many areas, such as training, education, business presentation, merchandising, and communications.multimedia, decision, studies, mining, architecture