scholarly journals Visual features influence thought content in the absence of overt semantic information

2019 ◽  
Author(s):  
Kathryn E Schertz ◽  
Omid Kardan ◽  
Marc Berman
Keyword(s):  
Cognitive Processes ◽  
Semantic Content ◽  
Visual Features ◽  
Edge Density ◽  
Low Level ◽  
Life Journey ◽  
Edge Content ◽  
High Level ◽  
Scene Identification ◽  
Selection Of

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content, in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation, by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Non-straight edge density is one visual feature which has been shown to influence many judgements about objects and landscapes, and has also been associated with thoughts of spirituality. We extend previous findings by showing that non-straight edges retain their influence on the selection of a “Spiritual & Life Journey” topic after scene identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher-order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.

scholarly journals Semantic Override of Low-level Features in Image Viewing – Both Initially and Overall

2008 ◽  
Vol 2 (2) ◽  
Author(s):  
Marcus Nyström ◽  
Kenneth Holmqvist
Keyword(s):  
Time Course ◽  
Movement Control ◽  
Semantic Content ◽  
Cognitive Factors ◽  
Edge Density ◽  
Low Level ◽  
Semantic Categories ◽  
Initial Fixation ◽  
High Level ◽  
Over Time

Guidance of eye-movements in image viewing is believed to be controlled by stimulus driven factors as well as viewer dependent higher level factors such as task and memory. It is currently debated what proportions these factors contribute to gaze guidance, and also how they vary over time after image onset. Overall, the unanimity regarding these issues is surprisingly low and there are results supporting both types of factors as being dominant in eye-movement control under certain conditions. We investigate how low, and high level factors influence eye guidance by manipulating contrast statistics on images from three different semantic categories and measure how this affects fixation selection. Our results show that the degree to which contrast manipulations affect fixation selection heavily depends on an image’s semantic content, and how this content is distributed over the image. Over the three image categories, we found no systematic differences between contrast and edge density at fixated location compared to control locations, neither during the initial fixation nor over the whole time course of viewing. These results suggest that cognitive factors easily can override low-level factors in fixation selection, even when the viewing task is neutral.

scholarly journals Robust Image Labeling Using Conditional Random Fields

2021 ◽  
Author(s):  
Maryam Nematollahi Arani
Keyword(s):  
Object Recognition ◽  
Mixture Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Gap ◽  
Visual Features ◽  
Image Labeling ◽  
Low Level ◽  
Robust Image ◽  
High Level

Object recognition has become a central topic in computer vision applications such as image search, robotics and vehicle safety systems. However, it is a challenging task due to the limited discriminative power of low-level visual features in describing the considerably diverse range of high-level visual semantics of objects. Semantic gap between low-level visual features and high-level concepts are a bottleneck in most systems. New content analysis models need to be developed to bridge the semantic gap. In this thesis, algorithms based on conditional random fields (CRF) from the class of probabilistic graphical models are developed to tackle the problem of multiclass image labeling for object recognition. Image labeling assigns a specific semantic category from a predefined set of object classes to each pixel in the image. By well capturing spatial interactions of visual concepts, CRF modeling has proved to be a successful tool for image labeling. This thesis proposes novel approaches to empowering the CRF modeling for robust image labeling. Our primary contributions are twofold. To better represent feature distributions of CRF potentials, new feature functions based on generalized Gaussian mixture models (GGMM) are designed and their efficacy is investigated. Due to its shape parameter, GGMM can provide a proper fit to multi-modal and skewed distribution of data in nature images. The new model proves more successful than Gaussian and Laplacian mixture models. It also outperforms a deep neural network model on Corel imageset by 1% accuracy. Further in this thesis, we apply scene level contextual information to integrate global visual semantics of the image with pixel-wise dense inference of fully-connected CRF to preserve small objects of foreground classes and to make dense inference robust to initial misclassifications of the unary classifier. Proposed inference algorithm factorizes the joint probability of labeling configuration and image scene type to obtain prediction update equations for labeling individual image pixels and also the overall scene type of the image. The proposed context-based dense CRF model outperforms conventional dense CRF model by about 2% in terms of labeling accuracy on MSRC imageset and by 4% on SIFT Flow imageset. Also, the proposed model obtains the highest scene classification rate of 86% on MSRC dataset.

TV Preference as a Technique for Selection of Reinforcers

1974 ◽  
Vol 35 (1) ◽  
pp. 107-113 ◽  
Author(s):  
Sebastian Striefel ◽  
Paul M. Smeets
Keyword(s):  
Educational Programs ◽  
Low Level ◽  
Simultaneous Choice ◽  
High Level ◽  
Selection Of

A technique for evaluating preference for television (TV) programs provided six low-level and six high-level retardates with a simultaneous choice of four keys, each programing a different consequence. The total time S depressed each of the four keys resulting in three different TV programs or no program (no audio or video) was recorded. Each consequence was continuously available and the consequence programmed by each key was switched every 2 min. All Ss spent most of the session responding for TV and preferred TV over no consequence. Five low-level and one high-level S showed a preference for a single program. The preference of one low-level and two high-level Ss varied from day to day and that of three high-level Ss changed one or more times during 10 sessions. One program can be repeated many times with low-level retardates before satiation occurs; whereas satiation occurs quickly with high-level retardates. TV preference might well be considered as a technique for selection of reinforcers. The technique has much to offer to those concerned with developing educational programs.

Abstractions in Intelligent Multimedia Databases

2013 ◽  
pp. 195-220
Author(s):  
Ranjan Parekh ◽  
Nalin Sharda
Keyword(s):  
Semantic Content ◽  
Multimedia Databases ◽  
Low Level ◽  
Layered Architecture ◽  
Automated Inference ◽  
Semantic Concepts ◽  
High Level ◽  
Intelligent Multimedia ◽  
Visual Keywords ◽  
Semantic Layer

Semantic characterization is necessary for developing intelligent multimedia databases, because humans tend to search for media content based on their inherent semantics. However, automated inference of semantic concepts derived from media components stored in a database is still a challenge. The aim of this chapter is to demonstrate how layered architectures and “visual keywords” can be used to develop intelligent search systems for multimedia databases. The layered architecture is used to extract meta-data from multimedia components at various layers of abstractions. While the lower layers handle physical file attributes and low-level features, the upper layers handle high-level features and attempts to remove ambiguities inherent in them. To access the various abstracted features, a query schema is presented, which provides a single point of access while establishing hierarchical pathways between feature-classes. Minimization of the semantic gap is addressed using the concept of “visual keyword” (VK). “Visual keywords” are segmented portions of images with associated low- and high-level features, implemented within a semantic layer on top of the standard low-level features layer, for characterizing semantic content in media components. Semantic information is however predominantly expressed in textual form, and hence is susceptible to the limitations of textual descriptors – viz. ambiguities related to synonyms, homonyms, hypernyms, and hyponyms. To handle such ambiguities, this chapter proposes a domain specific ontology-based layer on top of the semantic layer, to increase the effectiveness of the search process.

Neural Correlates of Fixated Low- and High-level Scene Properties during Active Scene Viewing

2020 ◽  
Vol 32 (10) ◽  
pp. 2013-2023
Author(s):  
John M. Henderson ◽  
Jessica E. Goold ◽  
Wonil Choi ◽  
Taylor R. Hayes
Keyword(s):  
Real World ◽  
Visual Analysis ◽  
Scene Perception ◽  
Active Vision ◽  
Semantic Content ◽  
Retrosplenial Cortex ◽  
Edge Density ◽  
Visual Stream ◽  
Lateral Occipital Complex ◽  
High Level

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.

scholarly journals Image Searching System

2021 ◽  
Vol 6 (2) ◽  
pp. 161-167
Author(s):  
Eduard Yakubchykt ◽  
◽  
Iryna Yurchak
Keyword(s):  
Semantic Content ◽  
Image Features ◽  
Multimedia Databases ◽  
Semantic Level ◽  
Low Level ◽  
Essential Properties ◽  
Calculation Algorithms ◽  
Similar Images ◽  
Definition Of ◽  
High Level

Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.

scholarly journals Feature-Specific Neural Reactivation during Episodic Memory

2019 ◽  
Author(s):  
Michael B. Bone ◽  
Fahad Ahmad ◽  
Bradley R. Buchsbaum
Keyword(s):  
Frontal Cortex ◽  
Visual Features ◽  
Semantic Features ◽  
Low Level ◽  
Visual Hierarchy ◽  
Strict Interpretation ◽  
Novel Approach ◽  
High Level ◽  
Hierarchical Representations

AbstractWhen recalling an experience of the past, many of the component features of the original episode may be, to a greater or lesser extent, reconstructed in the mind’s eye. There is strong evidence that the pattern of neural activity that occurred during an initial perceptual experience is recreated during episodic recall (neural reactivation), and that the degree of reactivation is correlated with the subjective vividness of the memory. However, while we know that reactivation occurs during episodic recall, we have lacked a way of precisely characterizing the contents—in terms of its featural constituents—of a reactivated memory. Here we present a novel approach, feature-specific informational connectivity (FSIC), that leverages hierarchical representations of image stimuli derived from a deep convolutional neural network to decode neural reactivation in fMRI data collected while participants performed an episodic recall task. We show that neural reactivation associated with low-level visual features (e.g. edges), high-level visual features (e.g. facial features), and semantic features (e.g. “terrier”) occur throughout the dorsal and ventral visual streams and extend into the frontal cortex. Moreover, we show that reactivation of both low- and high-level visual features correlate with the vividness of the memory, whereas only reactivation of low-level features correlates with recognition accuracy when the lure and target images are semantically similar. In addition to demonstrating the utility of FSIC for mapping feature-specific reactivation, these findings resolve the relative contributions of low- and high-level features to the vividness of visual memories, clarify the role of the frontal cortex during episodic recall, and challenge a strict interpretation the posterior-to-anterior visual hierarchy.

scholarly journals Electrophysiological correlates of the interplay between low-level visual features and emotional content during word reading

2018 ◽  
Author(s):  
Sebastian Schindler ◽  
Antonio Schettino ◽  
Gilles Pourtois
Keyword(s):  
Word Processing ◽  
Word Reading ◽  
Electrophysiological Study ◽  
Semantic Content ◽  
Stimulus Onset ◽  
Event Related Potential ◽  
Visual Features ◽  
Low Contrast ◽  
Attentional Processes ◽  
Low Level

Processing affectively charged visual stimuli typically results in increased amplitude of specific event-related potential (ERP) components. Low-level features similarly modulate electrophysiological responses, with amplitude changes proportional to variations in stimulus size and contrast. However, it remains unclear whether emotion-related amplifications during visual word processing are necessarily intertwined with changes in specific low-level features or, instead, may act independently.In this pre-registered electrophysiological study, we varied font size and contrast of neutral and negative words while participants were monitoring their semantic content. We examined ERP responses associated with early sensory and attentional processes as well as later stages of stimulus processing. Results showed amplitude modulations by low-level visual features early on following stimulus onset – i.e., P1 and N1 components –, while the LPP was independently modulated by these visual features. Independent effects of size and emotion were observed only at the level of the EPN. Here, larger EPN amplitudes for negative were observed only for small high contrast and large low contrast words. These results suggest that early increase in sensory processing at the EPN level for negative words is not automatic, but bound to specific combinations of low-level features, occurring presumably via attentional control processes.

Categorization

2020 ◽  
pp. 27-37
Author(s):  
Stephen K. Reed
Keyword(s):  
Baby Boomers ◽  
The Other ◽  
Social Categories ◽  
Elbow Angle ◽  
Low Level ◽  
High Level ◽  
Selection Of

Categories reduce the complexity of the environment, are the means by which objects are identified, reduce the need for constant learning, allow for the selection of an appropriate action, and support the organization of objects and events. The most typical members of categories share attributes with the other members of the category. Prototypes are the central members. Hierarchies are composed of subordinate (desk lamp), basic (lamp), and superordinate (furniture) categories. Social categories such as “ baby boomers” classify people but may be associated with misleading stereotypes. Action categories include event boundaries that mark the transition between actions. They are organized into low-level (elbow angle) and high-level (pouring milk) actions.

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