Abstract not found

The problem of representing the spatial structure of images, which arises in visual object processing, is commonly described using terminology borrowed from propositional theories of cognition, notably, the concept of compositionality. The classical propositional stance mandates representations composed of symbols, which stand for atomic or composite entities and enter into arbitrarily nested relationships.

visual structure: human data and a model

In this paper, we present a compositional boosting algorithm for detecting and recognizing 17 common image structures in low-middle level vision tasks. These structures, called “graphlets”, are the most frequently occurring primitives, junctions and composite junctions in natural images, and are arranged in a 3-layer And-Or graph representation. In this hierarchic model, larger graphlets are decomposed (in And-nodes) into smaller graphlets in multiple alternative ways (at Or-nodes), and parts are shared and re-used between graphlets. Then we present a compositional boosting algorithm for computing the 17 graphlets categories collectively in the Bayesian framework. The algorithm runs recursively for each node A in the And-Or graph and iterates between two steps – bottom-up proposal and top-down validation. The bottom-up step includes two types of boosting methods. (i) Detecting instances of A (often in low resolutions) using Adaboosting method through a sequence of tests (weak classifiers) image feature. (ii) Proposing instances of A (often in high resolution) by binding existing children nodes of A through a sequence of compatibility tests on their attributes (e.g angles, relative size etc). The Adaboosting and binding methods generate a number of candidates for node A which are verified by a top-down process in a way similar to Data-Driven Markov Chain Monte Carlo [18]. Both the Adaboosting and binding methods are trained off-line for each graphlet category, and the compositional nature of the model means the algorithm is recursive and can be learned from a small training set. We apply this algorithm to a wide range of indoor and outdoor images with satisfactory results. 1.

visual structure

This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered real-world scenes. We propose a sparse image representation based on localized feature histograms of salient regions. Category specific information is then aggregated by using relations from perceptual organization to form compositions of these descriptors. The underlying concept of image region aggregation to condense semantic information advocates for a statistical representation founded on graphical models. On the basis of this structure, objects and their constituent parts are localized. To complement the learned dependencies between compositions and categories, a global shape model of all compositions that form an object is trained. During inference, belief propagation reconciles bottomup feature-driven categorization with top-down category models. The system achieves a competitive recognition performance on the standard CalTech database.

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., 1987), it is argued that some universal principles may be attainable in cognitive science. Here we propose two examples: The simplicity principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale-invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. We illustrate how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath & Chater, in press), and briefly mention some extensions to models of identification and categorization. We also consider the scope and limitations of universal laws in cognitive science.

Model building by perturbation

Theories of object representation can be classified as structural, holistic or hybrid, depending on their approach to the mereology and compositionality of shapes. We tested the predictions of some of the current theories in three experiments, by quantifying the effects of various priming cues on response times to 3D objects. In experiment 1, there were two possible locations for the stimulus components: left-right and top-bottom. The prime could be identical to the stimulus, identical in location but with different parts, identical in the complement of differently located parts, or altogether different. Both location and part identity effects were significant. In experiment 2 we added a part-neutral (empty frame) prime condition; the effect of location, but not of part, remained significant. In experiment 3, which included an additional location-neutral prime condition, only the location effect, again, was significant. These findings are not entirely compatible either with the structu...

Abstract. To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation (as dictated, e.g., by the Minimum Description Length principle). Paradoxically, all the candidate features in this approach need to be known before statistics over them can be computed. This paradox may be circumvented by confining the repertoire of candidate features to actual scene fragments, which resemble the “what+where ” receptive fields found in the ventral visual stream in primates. We describe a single-layer network that learns such fragments from unsegmented raw images of structured objects. The learning method combines fast imprinting in the feedforward stream with lateral interactions to achieve single-epoch unsupervised acquisition of spatially localized features that can support systematic treatment of structured objects [1]. 1 A paradox and some ways of resolving it