Traditional and new principles of perceptual grouping

Perceptual grouping refers to the process of determining which regions and parts of the visual scene belong together as parts of higher order perceptual units such as objects or patterns. In the early 20th century, Gestalt psychologists identified a set of classic grouping principles which specified how some image features lead to grouping between elements given that all other factors were held constant. Modern vision scientists have expanded this list to cover a wide range of image features but have also expanded the importance of learning and other non-image factors. Unlike early Gestalt accounts which were based largely on visual demonstrations, modern theories are often explicitly quantitative and involve detailed models of how various image features modulate grouping. Work has also been done to understand the rules by which different grouping principles integrate to form a final percept. This chapter gives an overview of the classic principles, modern developments in understanding them, and new principles and the evidence for them. There is also discussion of some of the larger theoretical issues about grouping such as at what stage of visual processing it occurs and what types of neural mechanisms may implement grouping principles

The structure of experience, the nature of the visual, and type 2 blindsight

Unlike those with type 1 blindsight, people who have type 2 blindsight have some sort of consciousness of the stimuli in their blind field. What is the nature of that consciousness? Is it visual experience? I address these questions by considering whether we can establish the existence of any structural—necessary—features of visual experience. I argue that it is very difficult to establish the existence of any such features. In particular, I investigate whether it is possible to visually, or more generally perceptually, experience form or movement at a distance from our body, without experiencing colour. The traditional answer, advocated by Aristotle, and some other philosophers, up to and including the present day, is that it is not and hence colour is a structural feature of visual experience. I argue that there is no good reason to think that this is impossible, and provide evidence from four cases—sensory substitution, achomatopsia, phantom contours and amodal completion—in favour of the idea that it is possible. If it is possible then one important reason for rejecting the idea that people with type 2 blindsight do not have visual experiences is undermined. I suggest further experiments that could be done to help settle the matter

Spotlight the Negatives: A Generalized Discriminative Latent Model

Discriminative latent variable models (LVM) are frequently applied to various visual recognition tasks. In these systems the latent (hidden) variables provide a formalism for modeling structured variation of visual features. Conventionally, latent variables are de- fined on the variation of the foreground (positive) class. In this work we augment LVMs to include negative latent variables corresponding to the background class. We formalize the scoring function of such a generalized LVM (GLVM). Then we discuss a framework for learning a model based on the GLVM scoring function. We theoretically showcase how some of the current visual recognition methods can benefit from this generalization. Finally, we experiment on a generalized form of Deformable Part Models with negative latent variables and show significant improvements on two different detection tasks.Comment: Published in proceedings of BMVC 201

Contour integration: Psychophysical, neurophysiological, and computational perspectives

One of the important roles of our visual system is to detect and segregate objects. Neurons in the early visual system extract local image features from the visual scene. To combine these features into separate, global objects, the visual system must perform some kind of grouping operation. One such operation is contour integration. Contours form the outlines of objects, and are the first step in shape perception. We discuss the mechanism of contour integration from psychophysical, neurophysiological, and computational perspectives

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.Comment: AAAI'1

Feature-based encoding of face identity by single neurons in the human medial temporal lobe

Neurons in the human medial temporal lobe (MTL) that are selective for the identity of specific people are classically thought to encode identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortex is translated into a semantic representation of an individual person. Here, we show that some MTL neurons are selective to multiple different face identities on the basis of shared features that form clusters in the representation of a deep neural network trained to recognize faces. Contrary to prevailing views, we find that these neurons represent an individual’s face with feature-based encoding, rather than through association with concepts. The response of feature neurons did not depend on face identity nor face familiarity, and the region of feature space to which they are tuned predicted their response to new face stimuli. Our results provide critical evidence bridging the perception-driven representation of facial features in the higher visual cortex and the memory-driven representation of semantics in the MTL, which may form the basis for declarative memory