Download Food Images Png
Dawne Dam
Top 1 and Top 4 food classification accuracy for food items with three features fused together, DCD, MDSIFT and SCD. The top of the orange bar: Top 1 classification accuracy; the top of the blue bar: Top 4 classification accuracy.
The best flying food photography owes a lot of its success to excellent food styling and post-production. But doing a good job of these tasks relies on some really careful planning. Unlike other types of food photography, where you can often improvise quite a lot of the shot as you go along, flying food photography requires clear pre-visualization.
Once we have a clear idea of the desired result, we need to get this down on paper. Flying food photography is never done as a single shot, but instead made up of multiple elements photographed separately and then comped together in Photoshop. And only by sketching out our idea beforehand can we begin to identify the different elements our shot is composed of. From here we can begin putting everything together; making sure that we have all the material we need when we get to the comping and editing stage.
Well, it certainly can. And indeed it often does. But more often than not the food styling techniques behind a flying food photo are a combination of literally throwing things, and some very precise positioning or suspension of objects so that they look like they are flying.
There are a number of valid food styling techniques for positioning the floating elements of a flying food photo. You can suspend them from wires, or place them on rods made from Plexiglas, acrylic or another transparent material. The advantage of these two methods is that neither wires nor transparent rods are likely to cause ugly shadows over other areas of the image.
As should be pretty obvious by now, the food styling behind a shot like this is actually a lot more controlled than it looks. Still, with any flying food photography shoot, things are likely to get a little messy. So be sure to use plastic to cover up anything at risk before you start work.
As shown in Fig. 3 (upper panels), the mean valence and arousal ratings for the 60 food images used in the present study were rather diverse and spread over the valence-arousal space, forming a U-shaped pattern (see also Kaneko et al., 2018; Toet et al., 2018). A question is then whether the observed valence and arousal inter-trial effects hold for each image used, or whether the sequential effects observed were driven by a subset of the images used. For instance, the observed inter-trial effects could be driven predominantly by images whose rating was 50.0 (i.e., a rather neutral rating) and to a lesser extent by images rated at either ceiling or floor performance, as in the latter two cases there is simply less room for an increment or decrement, respectively. Here, and in the remaining part of the manuscript, we only take into account the ratings on the previous trial. Figure 3c and d illustrate the valence and arousal inter-trial effects for each image, as a function of the mean valence and mean arousal rating, respectively. Here, each circle represents a food image (like in Fig. 3a and b).
Regarding arousal, a positive serial dependence was observed for 59 out of 60 food images (i.e., 98.3%). The t-tests yielded a significant inter-trial effect for 66.7% of the images (see the red dots in Fig. 3d). Note that after FDR correction, a significant inter-trial effect was observed for 63.3% of the images. In contrast to the valence rating, the mean arousal rating never approached floor performance, and the optimal quadratic fit was more like a linear fit, with the food images above the fit showing a significant effect regardless the mean arousal rating.
We observed a valence as well as an arousal sequential effect for the vast majority of food images. However, this assimilative effect was not statistically significant for all images. An explanation for why this was not the case is that each image was only presented once to each participant, making the inter-trial effect rather noisy. In the case of valence, strongest inter-trial effects were observed for those images which were rated as rather neutral (i.e., valence preference was not clearly determined). In the case of arousal, there was no clear subset that drove the inter-trial effect.
In the present study, we investigated whether sequential effects play a role in the affective appraisal of food images. Here we showed for the first time that both the valence and arousal ratings for a food image are contingent upon the valence and arousal rating of a different food image on the previous trial, respectively. More specifically, a positive sequential dependence was observed with regard to the valence rating on the previous trial, indicating that the valence rating for a given image was higher when the previous food image was rated high on valence than when the previous food image was rated low on valence. A similar positive sequential effect was observed for the arousal rating. For the arousal rating, this assimilative effect was also observed up to three trials back, indicating that even the arousal rating of a food image three trials back affects the rating on the current trial. Interestingly, for the valence rating, we observed a negative (repulsive) effect with regard to the valence rating four trials back, indicating that the valence rating on a given trial was higher when a food image four trials back was rated low on valence than when it was rated high on valence.
In the present study, participants were always instructed to rate the food images. Therefore, it remains unclear whether simply viewing a food image is sufficient to observe an assimilative serial dependence or whether an explicit emotional task is a prerequisite to observe the effect. Recently, Van der Burg et al. (2019) found evidence for a positive serial dependence when participants were instructed to judge the attractiveness of a face from trial-to-trial. However, this positive serial dependence disappeared when the task alternated between a gender judgement task and attractiveness task even though the same stimuli were used. Thus, when the previous trial was associated with a different task, the serial dependence for facial attractiveness was absent. This supports the idea that positive serial dependencies (for at least facial attractiveness) are task specific (but see Van der Burg et al., 2018). However, other studies reported both a positive serial dependence (Fornaciai and Park, 2018) and a negative serial dependence (Van der Burg et al., 2013) when no judgement was made on the preceding trial (i.e., a passive trial), indicating that sequential dependencies can, in principle, arise rather effortlessly and automatically. The present study does not afford any conclusions about whether simply viewing an image is sufficient to elicit an assimilative serial dependence as the participants always performed the task.
Whereas nationality, age and BMI did not moderate the inter-trial effects, we find that gender had a significant effect on both serial dependencies. Overall, mean valence was rated higher by males than by females, while females gave higher mean arousal ratings than males. These results may reflect gender differences in cerebral responses to food images: while females show a greater activation than males in lateral prefrontal midline cortical regions (including the insula, a region implicated in arousal-related feelings of hunger, visceral sensations and current need states) males in contrast show a greater activation than females in the Amygdala (a primal limbic structure involved in determining the valence or attractiveness of food, Killgore and Yurgelun-Todd, 2010). Furthermore, it is also known that females report higher arousal ratings than males for some types of food (e.g. fruits and deserts, Padulo et al., 2017). While males and females both showed positive valence and arousal sequential dependencies, the inter-trial effects were significantly larger for males than for females. This is an intriguing finding as this may suggest that females apply a different strategy than males. For instance, a larger inter-trial effect may indicate that males may be more inclined to repeat the previous response than females (i.e. response bias). However, the food images were presented in a random order (such that positive and negative images were randomly shown), and we used an EmojiGrid, making it difficult for the participants to simply repeat the previous response, like in a two-alternative forced-choice (2AFC) paradigm. What is more likely, is that the serial dependencies for food images are due to sex differences in the brain (Cahill, 2006). For instance, it might be possible that short term memory consolidation for emotions is decreased in males compared to females, explaining why the most recent emotional experiences are more pronounced on the current trial for males than for females (see LaBar and Cabeza, 2006 for an interesting review regarding memory for emotions).
I have a version of this tripod from Manfrotto and it works very well for doing a normal overhead shot. By normal, I mean the type of shot where you are not showing an entire huge spread of food on a table.
Above you can see my camera set up. This is absolutely the easiest way to do an overhead food shot. My 90 degree arm extension is on the tripod. This attaches where your tripod head would have gone. You now have to take your tripod head and attach it to the end of the extension arm.
Table 1. Descriptions of high-ED foods, low-ED foods, and control office supplies included in two versions of a standardized image set developed to investigate the cognitive and neurobiological mechanisms of eating behaviors in 6- to 10-year-old children.
Figure 2. Example images of smaller and larger amounts of high-energy-dense foods. low-energy-dense foods and office supplies from a standardized image set developed investigate the cognitive and neurobiological mechanisms of eating behaviors in 6- to 10-year-old children.
dca57bae1f