Bio-Inspired Scheme for Classification of Visual Information

2011 ◽  
pp. 238-262
Author(s):  
Le Dong ◽  
Ebroul Izquierdo ◽  
Shuzhi Ge
Keyword(s):  
Selective Attention ◽  
Visual Information ◽  
Natural Images ◽  
Top Down ◽  
Biologically Inspired ◽  
Low Level ◽  
Research Problems ◽  
Knowledge Structuring ◽  
High Level

In this chapter, research on visual information classification based on biologically inspired visually selective attention with knowledge structuring is presented. The research objective is to develop visual models and corresponding algorithms to automatically extract features from selective essential areas of natural images, and finally, to achieve knowledge structuring and classification within a structural description scheme. The proposed scheme consists of three main aspects: biologically inspired visually selective attention, knowledge structuring and classification of visual information. Biologically inspired visually selective attention closely follow the mechanisms of the visual “what” and “where” pathways in the human brain. The proposed visually selective attention model uses a bottom-up approach to generate essential areas based on low-level features extracted from natural images. This model also exploits a low-level top-down selective attention mechanism which performs decisions on interesting objects by human interaction with preference or refusal inclination. Knowledge structuring automatically creates a relevance map from essential areas generated by visually selective attention. The developed algorithms derive a set of well-structured representations from low-level description to drive the final classification. The knowledge structuring relays on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone is a distribution mapping strategy involving two novel modules: structured low-level feature extraction using convolution neural network and topology preservation based on sparse representation and unsupervised learning algorithm. Classification is achieved by simulating high-level top-down visual information perception and classification using an incremental Bayesian parameter estimation method. The utility of the proposed scheme for solving relevant research problems is validated. The proposed modular architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.

Content-Based Image Retrieval

2011 ◽  
pp. 744-749
Author(s):  
Alan Wee-Chung Liew ◽  
Ngai-Fong Law
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Features ◽  
User Preferences ◽  
Multimedia Systems ◽  
Content Based Image Retrieval ◽  
Top Down ◽  
Low Level ◽  
Feature Based ◽  
High Level

With the rapid growth of Internet and multimedia systems, the use of visual information has increased enormously, such that indexing and retrieval techniques have become important. Historically, images are usually manually annotated with metadata such as captions or keywords (Chang & Hsu, 1992). Image retrieval is then performed by searching images with similar keywords. However, the keywords used may differ from one person to another. Also, many keywords can be used for describing the same image. Consequently, retrieval results are often inconsistent and unreliable. Due to these limitations, there is a growing interest in content-based image retrieval (CBIR). These techniques extract meaningful information or features from an image so that images can be classified and retrieved automatically based on their contents. Existing image retrieval systems such as QBIC and Virage extract the so-called low-level features such as color, texture and shape from an image in the spatial domain for indexing. Low-level features sometimes fail to represent high level semantic image features as they are subjective and depend greatly upon user preferences. To bridge the gap, a top-down retrieval approach involving high level knowledge can complement these low-level features. This articles deals with various aspects of CBIR. This includes bottom-up feature- based image retrieval in both the spatial and compressed domains, as well as top-down task-based image retrieval using prior knowledge.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

scholarly journals Characterization of Feedback Neurons in the High-Level Visual Cortical Areas That Project Directly to the Primary Visual Cortex in the Cat

2021 ◽  
Vol 14 ◽  
Author(s):  
Huijun Pan ◽  
Shen Zhang ◽  
Deng Pan ◽  
Zheng Ye ◽  
Hao Yu ◽  
...  
Keyword(s):  
Information Processing ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Higher Order ◽  
Top Down ◽  
Cortical Areas ◽  
Area 21A ◽  
High Level ◽  
Visual Cortical

Previous studies indicate that top-down influence plays a critical role in visual information processing and perceptual detection. However, the substrate that carries top-down influence remains poorly understood. Using a combined technique of retrograde neuronal tracing and immunofluorescent double labeling, we characterized the distribution and cell type of feedback neurons in cat’s high-level visual cortical areas that send direct connections to the primary visual cortex (V1: area 17). Our results showed: (1) the high-level visual cortex of area 21a at the ventral stream and PMLS area at the dorsal stream have a similar proportion of feedback neurons back projecting to the V1 area, (2) the distribution of feedback neurons in the higher-order visual area 21a and PMLS was significantly denser than in the intermediate visual cortex of area 19 and 18, (3) feedback neurons in all observed high-level visual cortex were found in layer II–III, IV, V, and VI, with a higher proportion in layer II–III, V, and VI than in layer IV, and (4) most feedback neurons were CaMKII-positive excitatory neurons, and few of them were identified as inhibitory GABAergic neurons. These results may argue against the segregation of ventral and dorsal streams during visual information processing, and support “reverse hierarchy theory” or interactive model proposing that recurrent connections between V1 and higher-order visual areas constitute the functional circuits that mediate visual perception. Also, the corticocortical feedback neurons from high-level visual cortical areas to the V1 area are mostly excitatory in nature.

scholarly journals Continuous Flash Suppression Known and Unknowns

2019 ◽  
Author(s):  
Ali Pournaghdali ◽  
Bennett L Schwartz
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Continuous Flash Suppression ◽  
Low Level ◽  
Experimental Configuration ◽  
High Level ◽  
The Impact ◽  
Nonconscious Perception

Studies utilizing continuous flash suppression (CFS) provide valuable information regarding conscious and nonconscious perception. There are, however, crucial unanswered questions regarding the mechanisms of suppression and the level of visual processing in the absence of consciousness with CFS. Research suggests that the answers to these questions depend on the experimental configuration and how we assess consciousness in these studies. The aim of this review is to evaluate the impact of different experimental configurations and the assessment of consciousness on the results of the previous CFS studies. We review studies that evaluated the influence of different experimental configuration on the depth of suppression with CFS and discuss how different assessments of consciousness may impact the results of CFS studies. Finally, we review behavioral and brain recording studies of CFS. In conclusion, previous studies provide evidence for survival of low-level visual information and complete impairment of high-level visual information under the influence of CFS. That is, studies suggest that nonconscious perception of lower-level visual information happens with CFS but there is no evidence for nonconscious highlevel recognition with CFS.

Connecting the dots without top-down knowledge: Evidence for rapidly-learned low-level associations that are independent of object identity

2018 ◽  
Author(s):  
Patrick Sadil ◽  
Kevin Potter ◽  
David E. Huber ◽  
Rosemary Cowell
Keyword(s):  
Visual Information ◽  
Naming Task ◽  
Object Identification ◽  
Matching Task ◽  
Lateral Part ◽  
Object Identity ◽  
Top Down ◽  
Object Processing ◽  
Episodic Memories ◽  
High Level

Knowing the identity of an object can powerfully alter perception. Visual demonstrations of this – such as Gregory’s (1980) hidden Dalmatian – affirm the existence of both top-down and bottom-up processing. We consider a third processing pathway: lateral connections between the parts of an object. Lateral associations are assumed by theories of object processing and hierarchical theories of memory, but little evidence attests to them. If they exist, their effects should be observable even in the absence of object identity knowledge. We employed Continuous Flash Suppression (CFS) while participants studied object images, such that visual details were learned without explicit object identification. At test, lateral associations were probed using a part-to-part matching task. We also tested whether part-whole links were facilitated by prior study using a part-naming task, and included another study condition (“Word”), in which participants saw only an object’s written name. The key question was whether CFS study (which provided visual information without identity) would better support part-to-part matching (via lateral associations) whereas Word study (which provided identity without the correct visual form) would better support part-naming (via top-down processing). The predicted dissociation was found, and confirmed by state-trace analyses. Thus, lateral part-to-part associations were learned and retrieved independently of object identity representations. This establishes novel links between perception and memory, demonstrating that (1) lateral associations at lower levels of the object identification hierarchy exist and contribute to object processing, and (2) these associations are learned via rapid, episodic-like mechanisms previously observed for the high-level, arbitrary relations comprising episodic memories.

Lexical Mediation between Sight and Sound in Speechreading

1992 ◽  
Vol 45 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Bruno H. Repp ◽  
Ram Frost ◽  
Elizabeth Zsiga
Keyword(s):  
Visual Information ◽  
Visual Input ◽  
Top Down ◽  
Auditory Cues ◽  
Speech Detection ◽  
Visual Match ◽  
Relative Shift ◽  
Low Level ◽  
Masking Noise ◽  
The Cross

In two experiments, we investigated whether simultaneous speech reading can influence the detection of speech in envelope-matched noise. Subjects attempted to detect the presence of a disyllabic utterance in noise while watching a speaker articulate a matching or a non-matching utterance. Speech detection was not facilitated by an audio-visual match, which suggests that listeners relied on low-level auditory cues whose perception was immune to cross-modal top-down influences. However, when the stimuli were words (Experiment 1), there was a (predicted) relative shift in bias, suggesting that the masking noise itself was perceived as more speechlike when its envelope corresponded to the visual information. This bias shift was absent, however, with non-word materials (Experiment 2). These results, which resemble earlier findings obtained with orthographic visual input, indicate that the mapping from sight to sound is lexically mediated even when, as in the case of the articulatory-phonetic correspondence, the cross-modal relationship is non-arbitrary.

Age-equivalent Top–Down Modulation during Cross-modal Selective Attention

2014 ◽  
Vol 26 (12) ◽  
pp. 2827-2839 ◽  
Author(s):  
Maria J. S. Guerreiro ◽  
Joaquin A. Anguera ◽  
Jyoti Mishra ◽  
Pascal W. M. Van Gerven ◽  
Adam Gazzaley
Keyword(s):  
Selective Attention ◽  
Auditory Processing ◽  
Visual Information ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Visual Modality ◽  
Auditory Modality ◽  
Auditory Information ◽  
Auditory Distraction ◽  
Top Down

Selective attention involves top–down modulation of sensory cortical areas, such that responses to relevant information are enhanced whereas responses to irrelevant information are suppressed. Suppression of irrelevant information, unlike enhancement of relevant information, has been shown to be deficient in aging. Although these attentional mechanisms have been well characterized within the visual modality, little is known about these mechanisms when attention is selectively allocated across sensory modalities. The present EEG study addressed this issue by testing younger and older participants in three different tasks: Participants attended to the visual modality and ignored the auditory modality, attended to the auditory modality and ignored the visual modality, or passively perceived information presented through either modality. We found overall modulation of visual and auditory processing during cross-modal selective attention in both age groups. Top–down modulation of visual processing was observed as a trend toward enhancement of visual information in the setting of auditory distraction, but no significant suppression of visual distraction when auditory information was relevant. Top–down modulation of auditory processing, on the other hand, was observed as suppression of auditory distraction when visual stimuli were relevant, but no significant enhancement of auditory information in the setting of visual distraction. In addition, greater visual enhancement was associated with better recognition of relevant visual information, and greater auditory distractor suppression was associated with a better ability to ignore auditory distraction. There were no age differences in these effects, suggesting that when relevant and irrelevant information are presented through different sensory modalities, selective attention remains intact in older age.

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