A semi-automated approach to balancing of bottom-up salience for predicting change detection performance

A Three-Feature Model to Predict Colour Change Blindness

Change blindness is a striking shortcoming of our visual system which is exploited in the popular ‘Spot the difference’ game, as it makes us unable to notice large visual changes happening right before our eyes. Change blindness illustrates the fact that we see much less than we think we do. In this paper, we introduce a fully automated model to predict colour change blindness in cartoon images based on image complexity, change magnitude and observer experience. Using linear regression with only three parameters, the predictions of the proposed model correlate significantly with measured detection times. We also demonstrate the efficacy of the model to classify stimuli in terms of difficulty.

Change Blindness Is Influenced by Both Contrast Energy and Subjective Importance within Local Regions of the Image

Our visual system receives an enormous amount of information, but not all information is retained. This is exemplified by the fact that subjects fail to detect large changes in a visual scene, i.e., change-blindness. Current theories propose that our ability to detect these changes is influenced by the gist or interpretation of an image. On the other hand, stimulus-driven image features such as contrast energy dominate the representation in early visual cortex (De Valois and De Valois, 1988; Boynton et al., 1999; Olman et al., 2004; Mante and Carandini, 2005; Dumoulin et al., 2008). Here we investigated whether contrast energy contributes to our ability to detect changes within a visual scene. We compared the ability to detect changes in contrast energy together with changes to a measure of the interpretation of an image. We used subjective important aspects of the image as a measure of the interpretation of an image. We measured reaction times while manipulating the contrast energy and subjective important properties using the change blindness paradigm. Our results suggest that our ability to detect changes in a visual scene is not only influenced by the subjective importance, but also by contrast energy. Also, we find that contrast energy and subjective importance interact. We speculate that contrast energy and subjective important properties are not independently represented in the visual system. Thus, our results suggest that the information that is retained of a visual scene is both influenced by stimulus-driven information as well as the interpretation of a scene.

The scene and the unseen: manipulating photographs for experiments on change blindness and scene memory: image manipulation for change blindness

The change blindness paradigm, in which participants often fail to notice substantial changes in a scene, is a popular tool for studying scene perception, visual memory, and the link between awareness and attention. Some of the most striking and popular examples of change blindness have been demonstrated with digital photographs of natural scenes; in most studies, however, much simpler displays, such as abstract stimuli or "free-floating" objects, are typically used. Although simple displays have undeniable advantages, natural scenes remain a very useful and attractive stimulus for change blindness research. To assist researchers interested in using natural-scene stimuli in change blindness experiments, we provide here a step-by-step tutorial on how to produce changes in natural-scene images with a freely available image-processing tool (GIMP). We explain how changes in a scene can be made by deleting objects or relocating them within the scene or by changing the color of an object, in just a few simple steps. We also explain how the physical properties of such changes can be analyzed using GIMP and MATLAB (a high-level scientific programming tool). Finally, we present an experiment confirming that scenes manipulated according to our guidelines are effective in inducing change blindness and demonstrating the relationship between change blindness and the physical properties of the change and inter-individual differences in performance measures. We expect that this tutorial will be useful for researchers interested in studying the mechanisms of change blindness, attention, or visual memory using natural scenes.

Artificially created stimuli produced by a genetic algorithm using a saliency model as its fitness function show that Inattentional Blindness modulates performance in a pop-out visual search paradigm

Salient stimuli are more readily detected than less salient stimuli, and individual differences in such detection may be relevant to why some people fail to notice an unexpected stimulus that appears in their visual field whereas others do notice it. This failure to notice unexpected stimuli is termed 'Inattentional Blindness' and is more likely to occur when we are engaged in a resource-consuming task. A genetic algorithm is described in which artificial stimuli are created using a saliency model as its fitness function. These generated stimuli, which vary in their saliency level, are used in two studies that implement a pop-out visual search task to evaluate the power of the model to discriminate the performance of people who were and were not Inattentionally Blind (IB). In one study the number of orientational filters in the model was increased to check if discriminatory power and the saliency estimation for low-level images could be improved. Results show that the performance of the model does improve when additional filters are included, leading to the conclusion that low-level images may require a higher number of orientational filters for the model to better predict participants' performance. In both studies we found that given the same target patch image (i.e. same saliency value) IB individuals take longer to identify a target compared to non-IB individuals. This suggests that IB individuals require a higher level of saliency for low-level visual features in order to identify target patches.

Change blindness images

Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.