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 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.

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 Investigating The Benefits of Online Running Groups On The Well-Being of Their Female Running Members

2021 ◽  
Author(s):  
Susan Hughes ◽  
Anne Moorhead
Keyword(s):  
Social Media ◽  
Research Design ◽  
Online Survey ◽  
Well Being ◽  
Mental Wellbeing ◽  
Perceived Benefits ◽  
Low Level ◽  
High Level ◽  
Moderate Range ◽  
Over Time

Abstract BackgroundOnline running communities are becoming increasingly prevalent within social media, and many groups have been exclusively established for female runners. The aim of this study was to investigate the wellbeing benefits and limitations of using Facebook running groups among women. MethodologyThe research design was a quantitative online survey. This survey was completed by 349 adult members of Facebook running groups for women. The online survey consisted of a validated scale, the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), to calculate individual wellbeing scores. Data were analysed using SPSS, conducting descriptives, frequencies and correlations tests. ResultsThe results showed that 14% of participants’ scores indicated a high level of wellbeing, 66% had a wellbeing score in the moderate range and 21% of participants scored in the range of low-level wellbeing. Participants specified how they perceived women’s running Facebook groups to benefit or limit areas of wellbeing. Responses indicated perceived benefits to sense of optimism, interest in other people and sense of feeling good about themselves. There were negligible perceived wellbeing limitations. Members who had been running for the longest reported to engage more frequently with the groups, which may suggest their identities as runners have strengthened over time. ConclusionOverall, this study clearly found that women’s running Facebook groups can provide wellbeing benefits for their members.

scholarly journals Voxelwise encoding models show that cerebellar language representations are highly conceptual

2021 ◽  
Author(s):  
Amanda LeBel ◽  
Shailee Jain ◽  
Alexander G. Huth
Keyword(s):  
Natural Language ◽  
Recent Work ◽  
Cognitive Processing ◽  
Language Processing ◽  
Motor Planning ◽  
Model Framework ◽  
Low Level ◽  
Semantic Categories ◽  
Feature Spaces ◽  
High Level

AbstractThere is a growing body of research demonstrating that the cerebellum is involved in language understanding. Early theories assumed that the cerebellum is involved in low-level language processing. However, those theories are at odds with recent work demonstrating cerebellar activation during cognitive tasks. Using natural language stimuli and an encoding model framework, we performed an fMRI experiment where subjects passively listened to five hours of natural language stimuli which allowed us to analyze language processing in the cerebellum with higher precision than previous work. We used this data to fit voxelwise encoding models with five different feature spaces that span the hierarchy of language processing from acoustic input to high-level conceptual processing. Examining the prediction performance of these models on separate BOLD data shows that cerebellar responses to language are almost entirely explained by high-level conceptual language features rather than low-level acoustic or phonemic features. Additionally, we found that the cerebellum has a higher proportion of voxels that represent social semantic categories, which include “social” and “people” words, and lower representations of all other semantic categories, including “mental”, “concrete”, and “place” words, than cortex. This suggests that the cerebellum is representing language at a conceptual level with a preference for social information.Significance StatementRecent work has demonstrated that, beyond its typical role in motor planning, the cerebellum is implicated in a wide variety of tasks including language. However, little is known about the language representations in the cerebellum, or how those representations compare to cortex. Using voxelwise encoding models and natural language fMRI data, we demonstrate here that language representations are significantly different in the cerebellum as compared to cortex. Cerebellum language representations are almost entirely semantic, and the cerebellum contains over-representation of social semantic information as compared to cortex. These results suggest that the cerebellum is not involved in language processing per se, but cognitive processing more generally.

Joint Developmental Trajectories of Bullying and Victimization from Childhood to Adolescence: A Parallel-Process Latent Class Growth Analysis

2020 ◽  
pp. 088626052093305
Author(s):  
Yueyue Zhou ◽  
Hao Zheng ◽  
Yiming Liang ◽  
Jiazhou Wang ◽  
Ru Han ◽  
...  
Keyword(s):  
Latent Class ◽  
Developmental Trajectories ◽  
School Bullying ◽  
Bullying Victimization ◽  
Low Level ◽  
Grade Difference ◽  
Group A ◽  
Trajectory Group ◽  
High Level ◽  
Over Time

Previous studies have shown that bullying and victimization can be experienced simultaneously by an individual and can change over time. Understanding the joint longitudinal development of the two is of great significance. We conducted a 4-year longitudinal study to examine the joint developmental trajectories of bullying and victimization, gender and grade differences in trajectory group membership, and changes in specific forms of bullying and victimization (verbal, relational, and physical bullying /victimization) in each trajectory group. A total of 775 children from China participated in our study. The average age of participants at the first wave was 10.90 years ( SD = 1.12), and boys accounted for 69.5% of the sample. Based on mean scores, four distinct joint developmental trajectories of bullying and victimization were found: the involvement group (both bullying and victimization increased from low to high over time, accounting for 7.6% of the total), the desisted group (both bullying and victimization decreased from high to low over time, 6.1%), the victimization group (victimization remained at a high level, whereas bullying remained at a low level for 3 years, 13.2%), and the noninvolved group (bullying and victimization remained at a stable low level, 73.1%). Boys were more likely than girls to belong to the involvement group, desisted group, and victimization group, whereas girls were more likely than boys to belong to the noninvolved group. There was no significant grade difference in the trajectory group. All forms of bullying/victimization were consistent with the overall trend and showed similar levels. These results have important implications for the prevention of and interventions for school bullying.

scholarly journals Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time

2019 ◽  
Vol 19 (3) ◽  
pp. 1 ◽  
Author(s):  
Heiko H. Schütt ◽  
Lars O. M. Rothkegel ◽  
Hans A. Trukenbrod ◽  
Ralf Engbert ◽  
Felix A. Wichmann
Keyword(s):  
Eye Movements ◽  
Top Down ◽  
Bottom Up ◽  
Low Level ◽  
Level Versus ◽  
High Level ◽  
Over Time

scholarly journals Disentangling the Independent Contributions of Visual and Conceptual Features to the Spatiotemporal Dynamics of Scene Categorization

2020 ◽  
Author(s):  
Michelle R. Greene ◽  
Bruce C. Hansen
Keyword(s):  
Time Course ◽  
Brain Activity ◽  
Similarity Measures ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Scene Categorization ◽  
Low Level ◽  
Related Potentials ◽  
Single Fixation ◽  
High Level

AbstractHuman scene categorization is characterized by its remarkable speed. While many visual and conceptual features have been linked to this ability, significant correlations exist between feature spaces, impeding our ability to determine their relative contributions to scene categorization. Here, we employed a whitening transformation to decorrelate a variety of visual and conceptual features and assess the time course of their unique contributions to scene categorization. Participants (both sexes) viewed 2,250 full-color scene images drawn from 30 different scene categories while having their brain activity measured through 256-channel EEG. We examined the variance explained at each electrode and time point of visual event-related potential (vERP) data from nine different whitened encoding models. These ranged from low-level features obtained from filter outputs to high-level conceptual features requiring human annotation. The amount of category information in the vERPs was assessed through multivariate decoding methods. Behavioral similarity measures were obtained in separate crowdsourced experiments. We found that all nine models together contributed 78% of the variance of human scene similarity assessments and was within the noise ceiling of the vERP data. Low-level models explained earlier vERP variability (88 ms post-image onset), while high-level models explained later variance (169 ms). Critically, only high-level models shared vERP variability with behavior. Taken together, these results suggest that scene categorization is primarily a high-level process, but reliant on previously extracted low-level features.Significance StatementIn a single fixation, we glean enough information to describe a general scene category. Many types of features are associated with scene categories, ranging from low-level properties such as colors and contours, to high-level properties such as objects and attributes. Because these properties are correlated, it is difficult to understand each property’s unique contributions to scene categorization. This work uses a whitening transformation to remove the correlations between features and examines the extent to which each feature contributes to visual event-related potentials (vERPs) over time. We found that low-level visual features contributed first, but were not correlated with categorization behavior. High-level features followed 80 ms later, providing key insights into how the brain makes sense of a complex visual world.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

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