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.

Bridging the Semantic Gap in Image Retrieval

2002 ◽  
pp. 14-36 ◽  
Author(s):  
Rhong Zhao ◽  
William I. Grosky
Keyword(s):  
Information Retrieval ◽  
Image Retrieval ◽  
Research Field ◽  
Visual Features ◽  
Semantic Features ◽  
Visual Data ◽  
Image Content ◽  
Low Level ◽  
Significant Research ◽  
High Level

The emergence of multimedia technology and the rapidly expanding image and video collections on the Internet have attracted significant research efforts in providing tools for effective retrieval and management of visual data. Image retrieval is based on the availability of a representation scheme of image content. Image content descriptors may be visual features such as color, texture, shape, and spatial relationships, or semantic primitives. Conventional information retrieval was based solely on text, and those approaches to textual information retrieval have been transplanted into image retrieval in a variety of ways. However, “a picture is worth a thousand words.” Image content is much more versatile compared with text, and the amount of visual data is already enormous and still expanding very rapidly. Hoping to cope with these special characteristics of visual data, content-based image retrieval methods have been introduced. It has been widely recognized that the family of image retrieval techniques should become an integration of both low-level visual features addressing the more detailed perceptual aspects and high-level semantic features underlying the more general conceptual aspects of visual data. Neither of these two types of features is sufficient to retrieve or manage visual data in an effective or efficient way (Smeulders, et al., 2000). Although efforts have been devoted to combining these two aspects of visual data, the gap between them is still a huge barrier in front of researchers. Intuitive and heuristic approaches do not provide us with satisfactory performance. Therefore, there is an urgent need of finding the latent correlation between low-level features and high-level concepts and merging them from a different perspective. How to find this new perspective and bridge the gap between visual features and semantic features has been a major challenge in this research field. Our chapter addresses these issues.

scholarly journals Content-based image retrieval for fabric images: A survey

2021 ◽  
Vol 23 (3) ◽  
pp. 1861
Author(s):  
Silvester Tena ◽  
Rudy Hartanto ◽  
Igi Ardiyanto
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Search Time ◽  
Computation Time ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Semantic Features ◽  
Low Level ◽  
Human Perceptions ◽  
High Level

In <span>recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce  </span>search time.

Triggering of transient LES relaxations in ferrets: role of sympathetic pathways and effects of baclofen

2000 ◽  
Vol 279 (1) ◽  
pp. G157-G162 ◽  
Author(s):  
Esther Staunton ◽  
Scott D. Smid ◽  
John Dent ◽  
L. Ashley Blackshaw
Keyword(s):  
Gastroesophageal Reflux ◽  
Lower Esophageal Sphincter ◽  
Glucose Infusion ◽  
Gastric Distention ◽  
Low Level ◽  
Before And After ◽  
Esophageal Sphincter ◽  
Splanchnic Mechanoreceptors ◽  
High Level

Activation of gastric vagal mechanoreceptors by distention is thought to be the trigger for transient lower esophageal sphincter relaxations (TLESR), which lead to gastroesophageal reflux. The contribution of higher-threshold gastric splanchnic mechanoreceptors is uninvestigated. GABABreceptor agonists, including baclofen, potently reduce triggering of TLESR by low-level gastric distention. We aimed to determine first whether this effect of baclofen is maintained at high-level distention and second the role of splanchnic pathways in triggering TLESR. Micromanometric/pH studies in conscious ferrets showed that intragastric glucose infusion (25 ml) increased triggering of TLESR and reflux. Both were significantly reduced by baclofen (7 μmol/kg ip) ( P < 0.05). When 40 ml of air was added to the glucose infusion, more TLESR occurred than with glucose alone ( P < 0.01). These were also reduced by baclofen ( P < 0.001). TLESR after glucose/air infusion were assessed before and after splanchnectomy (2–4, 9–11, and 23–25 days), which revealed no change. Baclofen inhibits TLESR after both low- and high-level gastric distention. Splanchnic pathways do not contribute to increased triggering of TLESR by high-level gastric distention.

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.

Getting the Spirit of Office Technologies! Does the Internal Organization Environment Support or Constrain?

2011 ◽  
pp. 194-210
Author(s):  
Huub J.M. Ruel
Keyword(s):  
Technology Implementation ◽  
Information Technologies ◽  
Organizational Environment ◽  
Adaptive Structuration Theory ◽  
Low Level ◽  
Adaptive Structuration ◽  
Work Autonomy ◽  
High Level ◽  
The Relationship

The relationship between Advanced Information Technologies (AIT) and organization is complex. Several theories and approaches try to get grip on this complex relationship. Adaptive Structuration Theory (AST) (DeSanctis and Poole, 1994) is one of them. It introduces the concept of spirit of AIT as an important determinant of AIT appropriation. AIT with a clear, coherent spirit will lead to a high level of AIT appropriation. But what about the role of the internal organizational environment? Does this constrain or support the role of the AIT’s spirit regarding AIT appropriation? This paper presents a study that aims to find an answer to this question. Three hypotheses were formulated and tested in four offices where employees used office technologies. Results confirm that a clear spirit is positively related to the level of appropriation as distinguished by DeSanctis and Poole (1994) and Poole and DeSanctis (1990). The results also make clear that this relationship is more positive among users who experienced a low level of change in the internal organizational environment along with the office technology implementation than among users who experienced a high level of change. Furthermore, the relationship is more positive among users with a low level of work autonomy than among users with a high level of work autonomy. This is not fully in line with our expectations. However, we think an explanation is available. We suppose that the answer lies in the office technology development process. All office technologies in this study’s offices were probably developed without anticipating the changes that office technology implementations might bring about in the internal organizational environment and with the aim to build systems that “reconfirm” the current “restrictive” work procedures. This study’s results once again indicate that office technology and other organizational components are interrelated.

Distractor-Aware Tracking with Multi-Task and Dynamic Feature Learning

2020 ◽  
pp. 2150031
Author(s):  
Weichun Liu ◽  
Xiaoan Tang ◽  
Chenglin Zhao
Keyword(s):  
Correlation Filter ◽  
Coarse Grained ◽  
Dynamic Feature ◽  
Semantic Features ◽  
Low Level ◽  
Fine Grained ◽  
Semantic Embedding ◽  
Training Stage ◽  
Online Tracking ◽  
High Level

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.

scholarly journals Developing a Theory of Surprise from Travelers’ Extraordinary Food Experiences

2017 ◽  
Vol 57 (2) ◽  
pp. 218-231 ◽  
Author(s):  
Sandhiya Goolaup ◽  
Cecilia Solér ◽  
Robin Nunkoo
Keyword(s):  
Social Context ◽  
Cultural Capital ◽  
Theoretical Contribution ◽  
Low Level ◽  
Extraordinary Experiences ◽  
High Level

The purpose of this research is to explore the extraordinary experiences of food tourists and to develop a theory of surprise in relation to a typology of food cultural capital. We draw on phenomenological interviews with 16 food tourists. We found that food tourists experienced surprise in different ways, depending on their food cultural capital. Food tourists who possessed a high level of cultural capital were surprised by the simplicity or complexity of the experience while those possessing a low level of cultural capital were surprised by the genuinity of the experience. Thus, we make an important theoretical contribution here as we learn that the resources food tourists possessed in the form of cultural capital conditioned the ways in which they conceived an extraordinary experience. More so, using the cultural capital perspective, we have also demonstrated the role of social context in contributing to creating an extraordinary experience.

Sign in / Sign up

Export Citation Format

Share Document