Computational framework for fusing eye movements and spoken narratives for image annotation.

Despite many recent advances in the field of computer vision, there remains a disconnect between how computers process images and how humans understand them. To begin to bridge this gap, we propose a framework that integrates human-elicited gaze and spoken language to label perceptually important regions in an image. Our work relies on the notion that gaze and spoken narratives can jointly model how humans inspect and analyze images. Using an unsupervised bitext alignment algorithm originally developed for machine translation, we create meaningful mappings between participants' eye movements over an image and their spoken descriptions of that image. The resulting multimodal alignments are then used to annotate image regions with linguistic labels. The accuracy of these labels exceeds that of baseline alignments obtained using purely temporal correspondence between fixations and words. We also find differences in system performances when identifying image regions using clustering methods that rely on gaze information rather than image features. The alignments produced by our framework can be used to create a database of low-level image features and high-level semantic annotations corresponding to perceptually important image regions. The framework can potentially be applied to any multimodal data stream and to any visual domain. To this end, we provide the research community with access to the computational framework.

Modeling eye movement patterns to characterize perceptual skill in image-based diagnostic reasoning processes.

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. In this article, we present a hierarchical probabilistic framework to discover the stereotypical and idiosyncratic viewing behaviors exhibited with expertise-specific groups. Through these patterned eye movement behaviors we are able to elicit the domain-specific knowledge and perceptual skills from the subjects whose eye movements are recorded during diagnostic reasoning processes on medical images. Analyzing experts' eye movement patterns provides us insight into cognitive strategies exploited to solve complex perceptual reasoning tasks. An experiment was conducted to collect both eye movement and verbal narrative data from three groups of subjects with different levels or no medical training (eleven board-certified dermatologists, four dermatologists in training and thirteen undergraduates) while they were examining and describing 50 photographic dermatological images. We use a hidden Markov model to describe each subject's eye movement sequence combined with hierarchical stochastic processes to capture and differentiate the discovered eye movement patterns shared by multiple subjects within and among the three groups. Independent experts' annotations of diagnostic conceptual units of thought in the transcribed verbal narratives are time-aligned with discovered eye movement patterns to help interpret the patterns' meanings. By mapping eye movement patterns to thought units, we uncover the relationships between visual and linguistic elements of their reasoning and perceptual processes, and show the manner in which these subjects varied their behaviors while parsing the images. We also show that inferred eye movement patterns characterize groups of similar temporal and spatial properties, and specify a subset of distinctive eye movement patterns which are commonly exhibited across multiple images. Based on the combinations of the occurrences of these eye movement patterns, we are able to categorize the images from the perspective of experts' viewing strategies in a novel way. In each category, images share similar lesion distributions and configurations. Our results show that modeling with multi-modal data, representative of physicians' diagnostic viewing behaviors and thought processes, is feasible and informative to gain insights into physicians' cognitive strategies, as well as medical image understanding.