Perception, 2013, volume 42, pages 122 – 125
doi:10.1068/p4201rvw
Reviews
Markov random fields for vision and image processing edited by A Blake, P Kohli, C Rother; MIT Press, Cambridge, MA, 2011, 472 pages, US $58.00 cloth (£39.95) ISBN 9780262015776 The flexibility of Markov Random Fields (MRFs) as a modelling tool, and their ability to encode long‑range correlations using short-range linkages, has made them popular across many disciplines. In their new book, Markov Random Fields for Vision and Image Processing, Andrew Blake, Pushmeet Kohli, and Carsten Rother aim to demonstrate their power when applied to image processing and computer vision problems. Covering the ground from classic algorithms through to state-of-the-art research, this book provides a detailed—if not always easily accessible—guide to MRFs in vision. We are first exposed to MRFs in the introductory chapter. New terminology and concepts are presented at a rapid pace; readers new to the subject may struggle. Subsequently, however, throughout the main sections of the book, the intention of the editors is made evident through the sensible structure adopted and the clear sense of progression. We are never presented with new material without first covering the necessary groundwork, and the complexity of the content increases at a sensible rate. The book has 5 sections: basic inference methods; basic MRF applications; continuous models, parameter learning, and advanced inference; higher order models; and advanced applications. As the majority of the content is focused on methods of inference and applications of MRFs to vision problems, this review will concentrate on these topics. The first section reviews classical inference methods for MRFs. Starting with a chapter on basic Graph Cut algorithms, the min‑cut/max‑flow formulation of the energy minimisation problem is introduced. This is a good starting point and needs to be understood for the following chapters which cover move making algorithms and loopy belief propagation and its linear programming variants. In logical fashion, these chapters give detailed descriptions of the classical algorithms, and an introduction to some of the state-of-the-art methods. The sections covering successful applications of MRFs are where the book excels. Split over different sections, the works described are varied in both topic and execution, and clearly convey the versatility and expressiveness—and resulting popularity—of the MRF. Section 2 shows us some of the powerful results that can be achieved by modelling vision problems using simple pairwise MRF models. Chapter 7 covers foreground/background segmentation using Graph Cuts. Due to the binary nature of this problem, this chapter is well placed to introduce MRF applications. The authors provide a thorough background of the topic before introducing us to their influential GrabCut algorithm and a detailed discussion of their results. The next chapter revisits segmentation, but models it as a continuous-valued energy function providing an interesting counterpoint to the previous chapter. We are also introduced to some of the problems associated with MRFs such as metrication, artefacts, and proximity bias. Chapter 9 concludes the discussion of foreground/background segmentation with an extension into the temporal domain. The authors demonstrate the flexibility of the MRF by combining a higher-order temporal prior to the traditional two-dimensional grid. The final chapters of this section move away from segmentation. Chapter 10 focuses on the super resolution problem and is the reader’s first exposure to multi-label applications. The authors model the problem in such a way that each label configuration corresponds to a high-resolution image, witha pairwise term ensuring smoothness. The final chapter in the section is a comparative analysis of minimisation methods applied to stereo matching, photo-montage, segmentation, and denoising. This chapter provides an excellent reference point for researchers deciding which inference method they should use in their application. Section 3 is composed of two groups of chapters, the first of which deals with continuous latent variable models. State-of-the-art variational methods and partial-differential-equation methods are presented in chapter 12 as an alternative to the traditional MRF formulation. Chapter 13 details parameter learning for continuous models, and chapter 14 provides techniques for performing inference on them.
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An abrupt return to discrete models in chapter 15 creates a slight confusion in the book’s generally sensible ordering. Nevertheless, this chapter provides an excellent discussion of some of the popular methods for parameters learning and provides easy-to-use methods for tuning MRF models. The final chapters of section 3 return to inference. Here it is modelled as an integer programming problem solvable by a relaxation. Chapter 16 reviews some of the popular convex relaxation methods, and chapter 17 introduces the state-of-the-art fast prime-dual method. These methods provide powerful techniques for finding approximate solutions to inference problems and demand at least a basic understanding of linear programming. More complex applications which use higher-order MRFs are introduced in section 4. The authors state that, “although higher-order MRFs have more expressive power, they make inference and learning problems much harder” (p. 295). Chapters 20 and 21 address this, proposing that maximum a posteriori estimation (MAP) solutions are possible in higher-order MRFs as long as the potentials are of a certain type. The chapters in this section present instances where the richness of higher-order MRF has produced good results when applied to image denoising and multi-class segmentation. The final section of the book reviews some advanced applications of MRFs. Examples are presented of complex models and larger vision systems with MRFs playing a central role. There is another look at stereo matching in chapter 23, with a novel method of modelling occlusion events; and image denoising is revisited in chapter 24 with a steerable random field in which the potentials are adapted depending on the local image structure. Chapters 25 and 26 apply MRFs to the popular topic of object recognition and scene understanding. Chapter 25 approaches the problem by extending the labelling to include object parts. These parts form the latent variables of a tree-structured MRF. Chapter 26 introduces the optical flow variant Sift Flow as a way of creating correspondences. Pixels form nodes of a grid which take on a discrete set of flow vectors. Smoothness of the output is ensured with pairwise potentials. The final chapter in the book is focused on modelling non-rigid motion for video editing. Again, we are shown the diversity of applications to which MRFs can be applied. It is in this book’s presentation of successful applications that the editors’ aim to “demonstrate the power of the Markov Random Field in vision” (p. 1) is most fully realised. For readers new to MRFs, these sections provide both motivation for and instruction on modelling their vision problems using MRFs. The range of applications covered is testament to the versatility of the MRF, and the results presented argue for their inclusion in any vision researcher’s toolbox. While sections of the book, including the introduction, will not prove easy reading for all, a solid foundation in probability will suffice to benefit from this book. Those readers experienced with MRFs or already using them in their work will find ideas for new avenues of research; and the chapters on parameter learning and inference methods will help them tune their models and improve their optimisation methods. At a price of £39.95/$58.00 Markov Random Fields for Vision and Image Processing offers good value for money with content spanning over 400 pages. Coupled with its impressive bibliography, this book will be a powerful tool to any researcher interested in MRFs. Oliver Moolan-Feroze Department of Computer Science, University of Bristol; e-mail:
[email protected] In your face: The new science of facial attraction by D Perrett; Palgrave Macmillan, Basingstoke, Hants, 2010, 300 pages, £14.99 cloth, £9.99 paper (US $26.00, $17.00) ISBN 9780230201293, 9780230340435 The Mona Lisa of Leonardo di Vinci, the most photographed face in the world, is an illustration of how we are fascinated by, and attracted to, faces. Faces represent a very special class of visual objects, with an analysis process different from other objects. Recognising and understanding another individual of the social group is a crucial ability for humans, and the face is the main source of information for this. A lot of research has been conducted on the perception of face identity and expression, but relatively little is known about aesthetic aspects of faces. David Perrett is one pioneer of the scientific study of facial attraction. He is Professor of Psychology at the University of St Andrews in Scotland and was among the first neuroscientists to discover the existence of brain cells selectively responding to faces and social stimuli. His work is well known in the face and vision research community as being highly original and groundbreaking, and the main focus of his team’s current research is on face perception and facial preferences in social settings.
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In this book, David Perrett sketches a brief history, ranging from the origins of the human face and face perception mechanisms, over the details and purpose of attractiveness, to the brain systems that might underlie and strengthen our sense of attractiveness in others. These topics are explained in a simple and interesting way, and are therefore accessible to laymen and beginners of science. The more experienced scientist might sometimes wish for more information on methodology and statistics. The first chapter gives an overview of how the human face has evolved from fishes, over mammals, to the face that we are nowadays attracted to. This evolution reflects an optimisation in social interaction; pupils surrounded by white allow for a better gaze detection, bare facial skin and a complex system of muscles facilitate the communication of expressions. The second chapter explains the brain mechanisms that underlie the perception of faces and of attractiveness. The author suggests that the latter resides in the “pleasure” centres of the brain, and therefore shows that it is lust, not a sense of beauty, that drives our perception of attractiveness. The brain is tuned to be interested in faces from the moment we are born, as is pointed out in the next chapter. Newborns are more interested in faces and face-like patterns than in other objects, but this interest is not (race-)specific. Throughout the following months and years infants form preferences for the faces of their own culture and family, thus establishing a sense of attraction to faces of their immediate environment. The chapter concludes with a somewhat puzzling finding: newborns show more interest in attractive than unattractive faces, as rated by adults. This observation stills needs to be reconciled with what was explained previously, namely that newborns’ preferences to specific faces have yet to be formed by the rewarding interaction with caretakers during development. The next two chapters report several studies of Perrett’s group in which they investigated the very characteristics of beauty. A universal agreement on beauty appears to lie in the averageness (reflecting a preference for what is familiar) and symmetry of faces. Thus, several unattractive faces become more attractive when being averaged into one composite face. However, Perrett’s group found that originally attractive faces lose in such a manipulation: hence there is something else to beauty on top of averageness. This extra seems to be femininity, both in female and male faces, and across all cultures. Femininity here refers to child-like features, such as large eyes and small mouth and nose. The question why people are attracted by some faces perceived as more beautiful is elaborated in chapters 6 and 7. Studies show that attractive people tend to be more successful, more likely to marry and have offspring. The relationship between beauty and health is inconclusive; Perrett reports studies that show the influence on facial fat (less attractive) and skin colour (oxygen- and carotenoid-rich skin is more attractive) on attractiveness ratings. However, people judge average weight in female faces as most healthy, but not as most attractive—underweight females are the prettiest. One might add that the opposite was true during the Renaissance: preferentially chubby women were portrayed, most likely reflecting the general beauty ideal of those times. The next chapter is concerned with the stability of beauty throughout life. The classification of faces into low, average, or high attractiveness is similar from infant to old female faces, while for male faces only masculinity judgments are stable. Chapter 9 tackles a very interesting historical question: can we tell a person’s personality from his or her face? Classic physiognomy has been proven wrong, but Perrett shows that some personality traits can be detected in faces, such as extroversion and sexual attitude. Linking this to the topic of the book, extrovert, intelligent, conscientious, agreeable, and emotionally stable personalities have been rated more attractive, while sexually committed faces are less attractive. Facial indicators of personality might in fact cause the personality traits by self-fulfilling prophecy, but also the opposite works: our moods are reflected in facial expressions, which imprint lasting changes in the form of wrinkles and lines. There is a tendency for partners in a relationship to look alike, which might reflect the fact that we seek, and are able to detect, similar personalities. The next chapter looks closer at whether couples really tend to be similar, since this might be disadvantageous with respect to variability in genetic makeup and therefore immune system efficiency. In fact, women prefer the smell of people with complementary immune systems, but they prefer faces with similar immune system properties. Generally, people seek partners with faces similar to their opposite-sex parent, therefore disproving the hypothesis of attractiveness subserving variability of genetic makeup. In the final chapter we are told about the neurobiological basis of attraction. Arousal makes people emotional and increases perceived attractiveness in a positive social interaction. This might lead to love, increased perceived attractiveness, and long-term relationships. Thus, love can modulate how
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attractive a face appears to us. Overall, Perrett presents a multitude of innovative studies from his own lab, which makes use of a wide range of approaches, including computer techniques to determine in an unbiased way which facial characteristics underlie judgments of attractiveness. These same computations are also used to transform face images and test the impact of such manipulations. While this basic methodology seems to be a reasonable scientific tool, its usage allows for human bias. As mentioned by the author himself, in the majority of his studies the faces depicted in pictures as well as the raters are white Caucasian students. Furthermore, many of the reported effects are small, explaining only 60% or less of people’s ratings. Perrett admits that such numbers can hardly be used to generalise his findings to a large (international) population, and he stresses the unpredictable nature of any individual person. Thus, there remain many unresolved issues: what is the point of beauty, and of being attracted to beauty? How can we explain cultural differences? How stable are these judgments over a lifetime? Maybe some progress could be obtained by investigating the current issues with neuroscientific techniques, such as neuroimaging or advanced psychophysics procedures. But this would require large samples in several different cultures and perhaps better understanding of active exploration and attention to faces and their features (such as eye movements). Thus, besides the originality and breadth of his book, the merit of the exploration of faces by David Perrett is to raise many new and intriguing questions that all constitute important avenues for future research. In Your Face is an easily readable book about some cognitive aspects of face perception. It presents a new perspective on human attraction, which might be most valuable for laymen and students interested in cognitive science and psychology rather than fellow scientists specialised in face processing. Anne Schobert1, Arnaud Saj1,2 1 Laboratory for Behavioral Neurology & Imaging of Cognition, Department of Neuroscience, Medical School, University of Geneva, Geneva, Switzerland; 2 Clinic of Neurology, University Hospital of Geneva, Geneva, Switzerland; e-mail:
[email protected],
[email protected] All books for review should be sent to the publishers marked for the attention of the reviews editor. Inclusion in the list of books received does not preclude a full review.
