Abstract Art Pics Download ((TOP))
Kanisha Alarie
Abstract Photography uses unconventional colors, shapes, angles, and blur to convey a feeling, emotion or sensation. Browse our collection of high-quality abstract photos and experimental art - HD and free for commercial use.
This can take a number of forms. In some cases, a photograph might aim to capture motion in a single image through the use of blurring. Other techniques include using special cameras that are able to perceive sources of light that are invisible to the human eye (such as infrared cameras). Other popular techniques of abstract imagery include macro photography (photography producing photographs of small items in a larger than life size) and digital photography techniques and filters.
For some photographers - the urge to abstract comes naturally. Knowing how to be abstract is about knowing how to strip context away from a scene and still convey the feeling, sensation or moment of that scene. Think less about the object itself and what it means in-context and more about how the lines and colours of the object make you feel.
When it comes to abstract photography - the way the lines and shades of an object play off of each other are the most important. Everyday items are built with a certain practicality in mind - so in general, the function takes precedent over the design.
This is a type of photography that makes use of extreme close-ups. Macro photography is not always abstract, but generally zooming in close to an object will help you to see the object as a collection of lines and shapes playing off each other rather than an object as whole. This will help when trying to convey an image of the object in an abstract manner.
Kirk Varnedoe, Institute for Advanced Study. This six-part series examines abstract art over a period of fifty years, beginning with a crucial juncture in modern art in the mid-1950s, and builds a compelling argument for a history and evaluation of late twentieth-century art that challenges the distinctions between abstraction and representation, modernism and postmodernism, minimalism and pop. The accompanying publication, Pictures of Nothing: Abstract Art since Pollock, is available for purchase from the Gallery Shops. In this first lecture, originally delivered at the National Gallery of Art on March 30, 2003, the distinguished art historian Kirk Varnedoe begins with Jackson Pollock at a key moment in the emergence of a new form of abstract art in the mid-1950s. Building on Ernst Gombrich's Mellon Lectures of 1956, Varnedoe begins by asking: Can there be a philosophy of abstract art as compelling as Gombrich's argument for illusionism? What is abstract art good for? And finally, what do we get out of abstract art?
It is an open question whether our findings generalize to any type of abstract image (given that style and origin were essentially random). ERPs do not allow measurement of brain responses elicited by an elaborate description of a painting. Thus, we were limited in the choice of single words that would effectively capture the meaning of an abstract image. This being said, our results can be likened to observations made in the case of music perception where music extracts have been shown to elicit similar semantic representations across listeners28,29. We tentatively conclude that concepts activated in long-term memory when one perceives abstract pictures are not purely idiosyncratic to the observer and, on the contrary, have generic relevance. This however, should not imply that individual differences in the appraisal of abstract images are negligible or even modest, since personal experience and aesthetic judgment likely interact in complex ways during image processing1,30. In sum, our findings make a compelling case for the idea that abstract images relate to abstract words similarly to the way in which concrete pictures relate to concrete words. This finding may account for the fact that abstract images and textures are widely used in advertising, communication media, and virtual environments, conveying much more it seems than mere aesthetic effects devoid of meaning.
Such a co-occurrence-based grounding mechanism appears to be straightforward for concrete words, which refer to clearly identifiable objects that can be perceived with our senses. However, it is far less obvious how grounding would be achieved when this is not the case (Barsalou 2016; Borghi et al. 2017). One prime example are abstract words such as libertarianism, jealousy, or childhood, which by definition do not refer to a distinct class of physical objects (for an overview, see Borghi et al. 2017). However, it should be noted that these issues already arise for concrete words whose referents one has never experienced directly, such as Atlantis or supernova (Günther, Dudschig, & Kaup, 2018; Günther and Nguyen, et al., 2020). The question of how can we achieve grounding in the absence of any direct sensorimotor experience is of central importance for theories of grounded cognition (Borghi et al. 2017); if they can account for only a fraction of words that are directly experienced, the usefulness and adequacy of grounded cognition theories as a general-level cognitive theory stands in question.
However, not all language-based vectors are the same: By definition, they have different dimensional values, and populate different neighborhoods of the induced semantic space (see Martínez-Huertas, Jorge-Botana, Luzón, & Olmos, in press, for a conceptually similar distinction between a specific dimensionality hypothesis and a semantic neighborhood hypothesis for the mapping between language and grounded information). Based on these properties, we can identify factors that potentially influence how well a vision-based representation can be predicted from a language-based representation. On the one hand, there could just be inherent, fundamental differences between language-based representations for concrete and abstract concepts which emerge naturally during the training of the language-based model. Initial evidence for this assumption is provided by Hollis and Westbury (2016), who demonstrate that the dimensions of language-based distributional vectors contain concreteness information that can be extracted using adequate mathematical methods.
In the following empirical studies, we test our model by deriving model-predicted images for words which are all outside the model training set (i.e., for which the model has no visual experience available). These model-predicted images are then paired with random control images. If our model matches human intuitions on which image better fits the word meaning (i.e., if participants systematically prefer the model prediction over the control image), this will demonstrate that our linguistic and perceptual experience provides the necessary information to establish a link between the two (and that our model provides one possible, simple account on how this can be achieved). The model will be tested in different conditions which we expect to influence model performance: On the one hand, we test the model on both concrete and abstract words; on the other hand, we test it on words that do or do not have training items (i.e., words for which visual experience is available) in their immediate neighborhood. Since these two variables (concreteness and visual neighbors) are normally highly correlated, we apply item selection procedures to disentangle them (see the Methods sections of Experiments 1, 2, and 3). This will allow us (a) to evaluate if the model generally succeeds in predicting visual representations from language-based representations, (b) to test which factors influence its ability to do so, and (c) to examine potential limits of our approach and identify conditions it is not able to handle.
Words The item set for this study was constructed by systematically manipulating the two independent variables discussed above: Concreteness and visual neighbors. The potential word candidates were taken from a large database containing concreteness ratings for 39,954 English words (Brysbaert et al. 2014). Only nouns were selected as potential candidates, since the ImageNet labels and therefore our training set only consisted of nouns. Further, we selected only medium-frequent words (SUBTLEX frequencies larger than 100 and smaller than 12,000; van Heuven et al. 2014) so that participants would most likely know the word, but not have extreme amounts of experience with it. The remaining 4845 words were classified as concrete and abstract through a median split.Footnote 2
We analyzed our data using a mixed-effect logistic regression (Jaeger 2008), using the packages lme4 (Bates et al. 2015) and lmerTest (Kuznetsova et al. 2017) for R (R Core Team 2017). In each analysis, we fitted a model to predict whether or not participants chose the model-predicted image (we measure the mapping performance as proportion of participants who chose this model-predicted image over the random control image). As fixed-effect predictors in the mixed-effect model, we employed concreteness (concrete vs. abstract), visual neighbors (far vs. near vs. maximum), and their two-way interactionFootnote 4. In addition, the model contained random intercepts for both participants and items. Although indicated by the experimental design (Barr et al. 2013), no by-participant random slopes for the fixed effects were included, since the model did not converge in that case (Bolker et al. 2009). To examine the significance of the fixed-effect terms, we then tested whether they could be removed from the model without a significant deterioration in model fit by employing likelihood-ratio tests.
In Table 2, we report the model parameters for the final models after removal of all non-significant predictors, alongside their conditional and marginal \(R^2\) (Bartoń 2018). A clear notable difference between the two Experiments, is that the model for Experiment 2 only contains an intercept which is significantly different from zero, indicating above-chance performance even the abstract/far condition. In Experiment 1, on the other hand, we observe above-chance performance in all conditions except for the abstract/far condition (see Fig. 2).
356178063d