Model Of Visual Selective Attention Research Articles

Chordiogram image descriptor based on visual attention model for image retrieval

Abstract A novel shape-based image retrieval is presented in this study. The foreground and background contents of images are strongly concealed, so they are represented individually to reduce their influence on each other in the proposed approach. The Otsu method is employed for segmenting the foreground from the background, and the saliency map and edge map are then clearly identified. Saliency reduces the time cost for feature computation, so salient edges are computed for the foreground and background images based on the selective visual attention model. Autocorrelation-based chordiogram image descriptors are computed separately for the foreground and background images, which are then combined in a hierarchical manner to form the proposed new descriptor. This approach avoids the concealment of foreground and background information, and the new descriptor is rich in geometric and its underlying texture, structure and spatial information. The proposed novel shape-based descriptor performs considerably better than conventional descriptors at content-based image retrieval. The proposed shape descriptor were extensively tested at image retrieval based on the Gardens Point Walking, St Lucia, University of Alberta Campus, Corel 10 ​k, and self-photographed image data sets. The precision and recall values were compared for the proposed and state-of-the-art-approaches when applied for shape-based image retrieval from these databases. The proposed shape descriptor provided satisfactory retrieval results in the experiments.

CONCEPT OF INFORMATION PROCESSES ORGANIZATION IN HUMAN VISUAL SYSTEM

Recent studies have shown that both traditional and modern (electronic) visual media significantly affect the processes occurring in the visual system of children and adolescents, iin the development of which the concept of controlled and uncontrolled elements is used. When studying the adaptation of the visual system to a visual load, ensuring the uptake, transmission and processing of visual information, its structural and functional organization is taken into account. The proposed summarized scheme of information processes in the visual system takes into account and significantly supplements the main provisions of the object−oriented model of selective visual attention, based on current methods of intelligent data processing. The results of the study indicate the complexity of the visual information transformation path from a visual stimulus to the creation and awareness of the image, occurring in the higher parts of the brain. The whole apparatus of encoding, transmitting, processing and perceiving visual information is useless if the guidance and focusing unit does not provide a clear and undistorted image of the objects of the outside world on the retina. In case of any malfunctions of the first unit or in the presence of defects in the visual system, the processes of finding compensation for this condition and modes of operation are started, which allows to obtain the most complete and high−quality perception of visual objects. These processes can lead to the formation of a visual system with sufficiently high visual functions or monocular one, if the compensation of existing problems will have a high "price". With the help of the proposed concept of organization of information processes in the visual system it is possible to assess the role of each of its considered blocks not only in the perception of visual information, but also the formation of this system in children and adolescents. Key words: visual system, information processes, visual information.

Excitatory versus inhibitory feedback in Bayesian formulations of scene construction.

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.

Efficient Target Object Localization Model Based on Biologically Motivated Visual Selective Attention

This work proposes a novel biologically motivated visual selective attention model for efficient visual searching, which is implemented by integrating three attention mechanisms: bottom-up attention, top-down attention, and spatial attention. Bottom-up attention generates salient locations by reflecting top-down biases as well as three primitive visual features: intensity, edge and color. Prototype-based object perception is proposed for top-down attention, in which a 3-D color histogram is applied to generate a prototype of the target object. And experience based spatial attention can determine acceleration to localize a target object, which is modeled by using memorized spatial location information updated by object-localization experience. In order to verify the performance of the proposed visual selective attention model, we apply the proposed model to a real application in pedestrian traffic signal detection, to be utilized as part of a blind guide system. The proposed selective attention model shows plausible performance in terms of accuracy and computation time while efficiently localizing pedestrian traffic signals.

Modeling the Effects of Perceptual Load: Saliency, Competitive Interactions, and Top-Down Biases.

A computational model of visual selective attention has been implemented to account for experimental findings on the Perceptual Load Theory (PLT) of attention. The model was designed based on existing neurophysiological findings on attentional processes with the objective to offer an explicit and biologically plausible formulation of PLT. Simulation results verified that the proposed model is capable of capturing the basic pattern of results that support the PLT as well as findings that are considered contradictory to the theory. Importantly, the model is able to reproduce the behavioral results from a dilution experiment, providing thus a way to reconcile PLT with the competing Dilution account. Overall, the model presents a novel account for explaining PLT effects on the basis of the low-level competitive interactions among neurons that represent visual input and the top-down signals that modulate neural activity. The implications of the model concerning the debate on the locus of selective attention as well as the origins of distractor interference in visual displays of varying load are discussed.

Simulating and Analyzing IF and LIF Neuron Models for Selective Visual Attention

By nature biological systems have the capacity to selectively guide their attention on some portion of visual scene and process only that selected information for further processing. The similar abilities are also required in artificial vision systems so that they can become more efficient and realistic as biological systems and in future manual supervisory and detection work can be replaced by artificial visual systems. In this paper concepts of visual information processing and selective visual attention are implemented and analyzed. The results from two neuron models viz. IF and LIF models are compared and an analysis is made. In visual selective attention model the attention is guided towards only the most salient object while ignoring the less salient objects this way the time taken to process the image is reduced as large amount of information which is not much relevant is rejected for further processing. The study made in this paper will be proving to very effective as it will enhance the reliability of the system. The visual system can be used in robot vision system, can be deployed in choke points of city and major functions to detect accidents etc. This concept is also useful in marketing to make a few second advertisements more effective.

Improved saliency toolbox/Itti model for region of interest extraction

The toolbox (STB)/Itti model is an outstanding computational selective visual attention model. In this paper, we propose an improved STB/Itti model to overcome the drawback of STB/Itti-its output saliency map is not large enough for region of interest (ROI) extraction. First, we employ a simplified pulse coupled neural network (PCNN) with a special input image, and more importantly, the PCNN does not require iterations. Subsequently, the PCNN takes the place of the winner-take-all network in STB/Itti. Experimental results show that the improved STB/Itti model works well for ROI extraction, with the mean area under the curve value of 0.8306 and robustness against noise and geometric attacks. The proposed model can greatly enhance the performances of both STB/Itti and PCNN model in image processing.

Temporal Trade-Off Effects in Sustained Attention: Dynamics in Visual Cortex Predict the Target Detection Performance during Distraction

Models of visual selective attention have suggested that the representation of specific features characterizing a target object is enhanced in the visual cortex, at the cost of competing task-irrelevant information. In psychophysical studies, however, such attentional enhancement has been shown to result in reduced perceptual sensitivity when maintained over periods of several seconds. Two experiments examined the relationship between target detection behavior and electrocortical facilitation in human visual cortex during sustained attention under competition, in near real time. Steady-state visual evoked potentials (ssVEPs) were used in a change detection paradigm, in which a stream of flickering grating stimuli containing target events was fully overlapping with distractor faces (experiment 1) or competing complex scenes (experiment 2), covering the same part of the visual field. Beamformer source localization was used to test plausibility of lower-tier visual cortex involvement in modulation of the ssVEP signal. Results of both experiments suggest that early overallocation of visual cortical resources to the attended stimulus stream is associated with rapid reduction of electrocortical facilitation and poor change detection across the entire trial. By contrast, temporally balanced dynamics in visual cortex predicted accurate change detection. Together, the present results support models of sustained selective attention that emphasize competition for resources in lower-tier visual cortex. These models can be extended by a temporal dimension, on which attentive behavior is characterized by frugal resource sharing across the viewing time.

Computational Modeling of Visual Selective Attention

An overview of a nerocomputational model of visual selective attention that has been properly implemented is presented in this abstract.Visual selective attention is a fundamental function of human cognition and a highly important brain mechanism, essential for the functioning of the human brain as a system. A comprehensive example of the role of human attention can be seen by noting that at each instant of conscious life, each person receives millions of external stimulations from his/her sensory systems, while only a limited amount is selected by attention for further processing that leads to conscious perception. If every stimulus was allowed to pass into perception, one would have been soon overflowed and in constant distraction. Adding to external stimulation all internal stimuli (e.g., thoughts), a person would end up in a totally unstable state. Selective attention is thus regarded as the main control mechanism, necessary for keeping the brain system in stability. It does so by filtering out any irrelevant information while at the same time advancing any vital stimulation to higher cortical areas for further processing.Attention can be oriented towards object or empty space either in a voluntary or an automatic manner. That is, attention can be guided by top-down and bottom-up processing as cognition can be regarded as a balance between internal motivations and external stimulations. Top-down or endogenous attention refers to the volitional modulation of neural activity that corresponds to an object or a location in space, and it functions in response to signals initiated by internal goals, that most likely originate in the parietal and frontal lobes of the brain (Buschman & Miller, 2007). Bottom-up or exogenous attention on the other hand is a faster and more automatic process that relies on the sensory saliency of stimuli registered by sub cortical structures and the primary sensory cortices (Corbetta & Shulman, 2002).Studying the brain from the computer scientists’ perspective has always being a great challenge, and is usually divided under two main paths within the computational intelligence (CI) field. On one, to understand and mimic in a sense the functionality of the human brain has triggered the design and implementation of artificial intelligent systems such as robotics, expert systems etc. On the other, the understanding of certain brain functions can be facilitated with the implementation of relevant cognitive computational models.Our objective is to develop a plausible and biologically realistic computational model of visual selective attention using tools from the field of computational intelligence and use it in engineering and other applications. In recent years, an increased interest in developing cognitive models for a variety of technology and engineering applications has been observed and more specifically, there has been much interest in the development of systems capable of simulating users’ attention and how these systems could be practically and effectively used.For example, a tendency towards practical systems that are based on human attention has been observed (Horvitz et al., 2003) while research on computer vision is as well heavily dependent on the principles of human attention (Sun & Fisher, 2003). To apply ideas and concepts of human attention in the Computational Intelligence area, it is necessary to develop relevant computational models that will allow pinpoint the functional details of this brain mechanism.In line with the above, we present here a brief description of a computational model of visual selective attention that we have designed and implemented.

Selective Attention and Consciousness: Investigating Their Relation Through Computational Modelling

The present study aimed at investigating the possible connection between conscious awareness and attention through the implementation of a neurocomputational model of visual selective attention. The development of the model was based on recent neurophysiological findings that document the synchronization of neural activity in cortical areas of the brain and the presence of competitive interactions among stimuli at the early stages of visual processing. The model was used to simulate the findings of a behavioural experiment conducted by Naccache et al. in Psychol Sci 13:416–424 (2002), which have sparked a debate on the possible links between attention and consciousness. The model reproduced closely the pattern of the behavioural data while incorporating mechanisms that take into account the neural activity representing the early visual processing of stimuli and the effects of top–down attention. Thus, by adopting a computational approach, we present a possible explanation of the findings at the neural level of information processing. The implications of these findings for the relation between attentional processes and conscious awareness are discussed.

Attention-driven salient edge(s) and region(s) extraction with application to CBIR

Selective visual attention plays an important role for humans to understand an image by intuitively emphasizing some salient parts. Such mechanism can be well applied in localized content-based image retrieval, due to the fact that in the context of CBIR, the user is only interested in a portion of the image and the rest of the image is irrelevant. Being aware of this, in this paper, the selective visual attention model (SVAM) is incorporated in the CBIR task to estimate the user's retrieval concept. In contrast with existing learning based retrieval algorithms which need relevance feedback strategy to get user's high-level semantic information, the proposed method does not need any user's interaction to provide the training data. From this point of view, our method can be regarded as the purely bottom-up manner while learning based algorithms belong to the top-down manner. Specifically, an improved saliency map computing algorithm is employed first. Then, based on the saliency map, an efficient salient edges and regions detection method is introduced. Moreover, the concepts of salient edge histogram descriptors (SEHDs) and salient region adjacency graphs (SRAGs) are proposed, respectively, for images’ similarity comparison. Finally, an integrated strategy is adopted for content-based image retrieval. Experiments show that the proposed algorithm can characterize the human perception well and achieve satisfying retrieval performance.

Selective attention model with spiking elements

A new biologically plausible model of visual selective attention is developed based on synaptically coupled Hodgkin-Huxley neurons. The model is designed according to a two-layer architecture of excitatory and inhibitory connections which comprises two central neurons and a population of peripheral neurons. Two types of inhibition from the central neurons are present: fixed inhibition which is responsible for the formation of the attention focus, and short-term plastic inhibition which is responsible for the shift of attention. The regimes of synchronous dynamics associated with the development of the attentional focus are studied. In particular, the regime of partial synchronization between spiking activity of the central and peripheral neurons is interpreted as object selection to the focus of attention. It is shown that peripheral neurons with higher firing rates are selected preferentially by the attention system. The model correctly reproduces some observations concerning the mechanisms of attentional control, such as the coherence of spikes in the population of neurons included in the focus of attention, and the inhibition of neurons outside the focus of attention. Sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model in the frequency domain in both a formal example and a real image.

A Biologically Inspired System for Classification of Natural Images

A system for visual information analysis and classification based on a biologically inspired visual selective attention model with knowledge structuring is presented. The system is derived from well-known analogous processes in the visual system of primates and inference procedures of the human brain. It consists of three main units: biologically inspired visual selective attention, knowledge structuring, and clustering of visual information. The biologically inspired visual selective attention unit closely follow the mechanisms of the visual what pathway and where pathway in the primates' brain. It uses a bottom-up approach to generate a salient area based on low-level features extracted from natural images. The scale selection to determine suitable size of salient areas uses a maximum entropy approach. This unit also contains a low-level top-down selective attention module that performs decisions on interesting objects by human interaction. In this module, a reinforcement/inhibition mechanism is exploited. The knowledge structuring unit automatically creates a relevance map from salient image areas generated by the biologically inspired unit. It also derives a set of well-structured representations from low-level descriptions to drive the final classification. The knowledge structuring unit relys on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone of this unit is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a growing cell structure network. The third unit of the system classification is achieved by simulating high-level top-down visual information perception and clustering using an incremental Bayesian parameter estimation method. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available

Selection-for-action in visual search

Grasping an object rather than pointing to it enhances processing of its orientation but not its color. Apparently, visual discrimination is selectively enhanced for a behaviorally relevant feature. In two experiments we investigated the limitations and targets of this bias. Specifically, in Experiment 1 we were interested to find out whether the effect is capacity demanding, therefore we manipulated the set-size of the display. The results indicated a clear cognitive processing capacity requirement, i.e. the magnitude of the effect decreased for a larger set size. Consequently, in Experiment 2, we investigated if the enhancement effect occurs only at the level of behaviorally relevant feature or at a level common to different features. Therefore we manipulated the discriminability of the behaviorally neutral feature (color). Again, results showed that this manipulation influenced the action enhancement of the behaviorally relevant feature. Particularly, the effect of the color manipulation on the action enhancement suggests that the action effect is more likely to bias the competition between different visual features rather than to enhance the processing of the relevant feature. We offer a theoretical account that integrates the action–intention effect within the biased competition model of visual selective attention.

Neural network model of selective visual attention using Hodgkin?Huxley equation

We propose a mathematical model of selective visual attention using a two-layered neural network with neurons described by the Hodgkin-Huxley equation in order to investigate part of the assumption proposed by Desimone and Duncan. The neural network consists of a layer of hippocampal formation and of visual cortex. A frequency of firing and a firing time for each neuron and also a correlation of the firing times between neurons are calculated numerically to clarify an attention state, a nonattention state, and an attention shift. We find that synchronous phenomena occur not only for the frequency but also for the firing time between the neurons in the hippocampal formation and those in a part of the visual cortex in our model. It also turns out that the attention shift is performed quickly in our model.

Associative knowledge controls deployment of visual selective attention.

According to some models of visual selective attention, objects in a scene activate corresponding neural representations, which compete for perceptual awareness and motor behavior. During a visual search for a target object, top-down control exerted by working memory representations of the target's defining properties resolves competition in favor of the target. These models, however, ignore the existence of associative links among object representations. Here we show that such associations can strongly influence deployment of attention in humans. In the context of visual search, objects associated with the target were both recalled more often and recognized more accurately than unrelated distractors. Notably, both target and associated objects competitively weakened recognition of unrelated distractors and slowed responses to a luminance probe. Moreover, in a speeded search protocol, associated objects rendered search both slower and less accurate. Finally, the first saccades after onset of the stimulus array were more often directed toward associated than control items.

A Smart-Scanning Analog VLSI Visual-Attention System

We have implemented a hardware model of selective visual attention within the neuromorphic, analog VLSI paradigm. The system includes a highly-parallel winner-take-all selection with excitatory and inhibitory influences. The selection specifies positions of attention based on an array of intensity levels, which comprise a primitive saliency map. The excitation and inhibition control the strategy for shifts of attention from one position to the next. The combination of these fundamental building blocks demonstrates emergent properties that can be observed in real time due to the parallel hardware implementation. The system behaves as a smart-scanning sensor array. The basic characteristics of the scanning pattern are controlled by setting a number of analog parameters. In this paper we describe the system, focusing on the role that inhibition plays in the redirection of attention. We show experimental results from one-dimensional implementations of the hardware model. Analysis that explains the expected behavior for the two-element mode of operation is presented. The theoretical predictions are compared to experimental results.

Attentional selection of objects or features: evidence from a modified search task.

Three experiments examined the domain of visual selective attention (i.e., feature-based selection vs. object-based selection). Experiment 1 extended the requirements of the visual search task by requiring a feature discrimination response to target elements presented for short durations (30-105 msec). Targets were embedded in 47 distractor elements and were defined by either a distinct color or a distinct orientation. Observers made a discrimination response to either the target's color or its orientation. When the target-defining feature and the feature to be discriminated were the same (matched conditions), accuracy was enhanced relative to when these features belonged to separate dimensions (mismatched conditions). In Experiment 2, similar results were found in a task in which the target-defining dimension varied from trial to trial and observers performed both color and orientation discriminations on every trial. The results from these two experiments are consistent with feature-based attentional selection, but not with object-based selection. Experiment 3 extended these findings by showing that the effect is rooted in the overlap between target and distractor values in the stimulus set. The results are discussed in the context of recent models of visual selective attention.

Neural representation of objects in space: a dual coding account.

I present evidence on the nature of object coding in the brain and discuss the implications of this coding for models of visual selective attention. Neuropsychological studies of task-based constraints on: (i) visual neglect; and (ii) reading and counting, reveal the existence of parallel forms of spatial representation for objects: within-object representations, where elements are coded as parts of objects, and between-object representations, where elements are coded as independent objects. Aside from these spatial codes for objects, however, the coding of visual space is limited. We are extremely poor at remembering small spatial displacements across eye movements, indicating (at best) impoverished coding of spatial position per se. Also, effects of element separation on spatial extinction can be eliminated by filling the space with an occluding object, indicating that spatial effects on visual selection are moderated by object coding. Overall, there are separate limits on visual processing reflecting: (i) the competition to code parts within objects; (ii) the small number of independent objects that can be coded in parallel; and (iii) task-based selection of whether within- or between-object codes determine behaviour. Between-object coding may be linked to the dorsal visual system while parallel coding of parts within objects takes place in the ventral system, although there may additionally be some dorsal involvement either when attention must be shifted within objects or when explicit spatial coding of parts is necessary for object identification.

Spatial extinction on double asynchronous stimulation

Despite the fact that visual extinction is widely considered a space-based disturbance of selective attention, there has been little theoretical consensus about the nature of its pathogenic mechanism. A specific disruption in the ability to disengage attention from ipsilesional stimuli, or a loss of weight with which contralesional objects compete for visual selection, have been hypothesized to account for the disorder. We tested the merits of these two explanations in a right-hemisphere-lesioned patient. FB, who failed to recognize a contralesional target only when it was shown concurrently to an ipsilesional target (i.e. visual extinction). His task was to report two target letters presented in rapid succession to the left and right of the fixation point. The order of stimulus presentation (Left-First vs Right-First), and the intertarget interval (stimulus onset asynchrony) were varied systematically. We showed that contralesional extinction may occur for successively presented targets, not just for stimuli displayed at the same time. Of most importance, FB was seriously and equally impaired in dealing with a contralesional stimulus when this either preceded the ipsilesional stimulus or followed it by an interval less than about 600 msec. The data appear to contradict the disengagement hypothesis, which predicted a substantial reduction of extinction when a stimulus was displayed first into the lesioned side of space. We suggest that a competitive model of visual selective attention fits the data quite well.