Itti Model Research Articles

Through the lens of color: Relationships between color properties and visual perception of wood (Acer.) figures

The wood figure is a crucial wood trait that contributes to its versatile applications, drawing attention and inspiring imagination. This study aims to elucidate the impact of color attributes on the visual perception of wood figures. Initially, using intelligent methods (interactive color palettes), we colored six typical figures of natural wood (Acer.) as sample images. These images were characterized by entropy values, and assessed by simulated visual saliency (ITTI model) and subjective questionnaires. Finally, we discussed how different color quantities and schemes affect complexity and visual saliency of wood figures. The results indicate that changes in color attributes significantly impact the visual perception of wood figures. When the number of replacement colors is four, both the change rate in image complexity and the mean visual saliency are at their highest, at 24% and 0.335 respectively. Among the colors red, yellow, and green, red tends to influence the intensity of visual saliency more, the subjective complexity of green is higher. In addition, we found that more complex figures are less affected by changes in color attributes, and people can also distinguish the degree of difference between figure types well.

Image Perceptual Hashing for Content Authentication Based on Geometric Invariant Vector Distance

Image perceptual hashing is broadly applied in image content authentication, recognition, retrieval, and social media hotspot event detection. An image authentication algorithm is put forward based on the Itti visual saliency model and geometric invariant vector distance. To begin with, the image is preprocessed and weighted by the Itti model and contourlet transform. After that, the weighted image is randomly divided into blocks, and the image feature vector is constructed by calculating the geometric invariant vector distance on both Hu invariant moment vector and maximum singular value vector of the random blocks. In the end, the feature vector is quantized and encrypted to generate the ultimate hash. Experimental results illustrate that when the threshold T = 70, the true positive rate P T P R for duplicate images stands at 0.96574, while the false rate P F P R of different images is merely 0.0224, with the total error rate reaching the minimum value (0.0566). Furthermore, the AUC value of the proposed algorithm is 0.9951, which is higher than that of the comparison algorithms, indicating that the algorithm has better performance than other state-of-the-art algorithms in terms of various visual content-preserving attacks.

Robust Image Hashing With Saliency Map And Sparse Model

Abstract Image hashing is an effective technology for extensive image applications, such as retrieval, authentication and copy detection. This paper designs a new image hashing scheme based on saliency map and sparse model. The major contributions are twofold. The first contribution is the construction of a weighted image representation by combining a visual attention model called Itti model and the matrix of color vector angle (CVA). Since the Itti model can efficiently detect saliency map and CVA fully captures color information of image, they contribute to a visually robust and discriminative image representation. The second contribution is the hash extraction from the weighted image representation via sparse model. A classical sparse model called robust principal component analysis is exploited to decompose the weighted image representation into a low-rank component and a sparse component. As the low-rank component can describe intrinsic structure of image, hash calculation with low-rank component can achieve good discrimination. The efficiencies of the proposed scheme are validated by extensive experiments with open databases. The results demonstrate that the proposed scheme is superior to some state-of-the-art schemes in terms of classification performance between robustness and discrimination.

Robust image hashing with compressed sensing and ordinal measures

Image hashing is an efficient technology for processing digital images and has been successfully used in image copy detection, image retrieval, image authentication, image quality assessment, and so on. In this paper, we design a new image hashing with compressed sensing (CS) and ordinal measures. This hashing method uses a visual attention model called Itti model and Canny operator to construct an image representation, and exploits CS to extract compact features from the representation. Finally, the CS-based compact features are quantized via ordinal measures. L2 norm is used to judge similarity of hashes produced by the proposed hashing method. Experiments about robustness validation, discrimination test, block size discussion, selection of visual attention model, selection of quantization scheme, and effectiveness of the use of ordinal measures are conducted to verify performances of the proposed hashing method. Comparisons with some state-of-the-art algorithms are also carried out. The results illustrate that the proposed hashing method outperforms some compared algorithms in classification according to ROC (receiver operating characteristic) graph.

Optimized Rate Control Algorithm of High-Efficiency Video Coding Based on Region of Interest

Aiming at the problems that the strategy of target bit allocation at the CTU layer has deviations from the human subjective observation mechanism, and the update phase of parametric model has a higher complexity in the JCTVC-K0103 rate control algorithm of ITU-T H.265/high efficiency video coding (HEVC) standard. Optimized rate control (ORC) algorithm of ITU-T H.265/HEVC based on region of interest (ROI) is proposed. Firstly, the algorithm extracts the region of interest of video frames based on time and space domains by using the improved Itti model. Then, the weight of target bits w is recalculated based on space-time domains to improve the rate control accuracy, and the target bits are distributed based on ROI by the adaptive weight algorithm once again to make the output videos more attuned with the human visual attention mechanism. Finally, the quasi-Newton algorithm is used to update the rate distortion model, which reduces the computational complexity in the update phase of the parametric model. The experimental results show that the ORC algorithm can obtain a better subjective quality in the compressed results with less bit error compared with the other two algorithms. Meanwhile, the rate distortion performance of the ORC algorithm is better on the premise of guaranteeing rate control performance.

Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

The emergence of 4D heart images makes the data volume of the images multiply. It is more urgent to require an effective and fast segmentation algorithm. Therefore, a heart image can be accurately segmented from a large amount of image data and an area of interest can be extracted The segmentation algorithm is very necessary. Based on the segmentation and recognition of medical images, this paper proposes a neural network and image saliency based on the obvious difference between the heart image and other tissues in the slice, and the high similarity between adjacent slices in the CT image sequence. Fully automatic segmentation algorithm and 3D visual reconstruction is the segmented heart image. Convolutional neural network is a special deep neural network model of artificial intelligence. Its connections between neurons are not fully connected. The weights of connections between certain neurons in the same layer are shared, and the network model is reduced. The complexity reduces the number of weights. The use of visual saliency techniques to achieve cardiac segmentation based on CT images. An image saliency detection algorithm is adopted to introduce the image segmentation algorithm based on the saliency technique. In this paper, considering the PET image as grayscale image with low resolution, an improved Itti model and an improved GrabCut image segmentation algorithm are proposed to solve the problem of the original algorithm in grayscale image. At the same time, the operation steps of the user division area are cancelled, and the automatic processing is realized, and the running time of the algorithm is improved while optimizing the image segmentation effect. The convolutional neural network is constructed to realize the positioning function of the heart in the image. The original cardiac CT image is cropped by the positioning result, and some non-target areas are removed. A stacking noise reduction self-coding network is constructed, and the network is manually segmented. Training, realize the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart, and finally realize the segmentation of the heart image based on the classification result. The results of the above segmentation algorithm are quantitatively evaluated and analyzed with the artificial segmentation results, and the segmentation results are visually reconstructed by surface rendering and volume rendering. The algorithm has better accuracy, reliability and higher. The segmentation efficiency is more simplified for user operations.

Salient object detection via reciprocal function filter

Salient object detection (SOD) has been attracting a lot of interest, and recently many computational models have been developed. Here, the authors formulate a SOD model, in which saliency map is computed as a combination of the colour, its distribution-based saliency and orientation saliency. Similar to traditional SODs, the proposed method is based on super-pixel segmentation and super-pixel utilises both colour and its distribution-based saliency to generate a coarse saliency map. However, distinct from traditional SODs, authors further use orientation contrast to optimise the coarse saliency map to obtain an improved saliency map. Authors’ contributions are twofold. First, the authors combine colour uniqueness and its distribution with local orientation information (LOI) used in Itti's model to effectively improve profiles of salient regions. Second, a reciprocal function is defined to substitute the Gabor function used in LOI, and the authors have proved that the substitution could detect relatively homogeneous and uniform regions at the boundary of salient object, whereas it is what the traditional models lack. Authors’ approach significantly outperforms state-of-the-art methods on four benchmark datasets, while the authors demonstrate that the proposed method runs as fast as most existing algorithms.

A novel visual attention method for target detection from SAR images

Synthetic Aperture Radar (SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process. In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio (SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency-based models have not been widely applied for prediction of infant gaze patterns. In this paper, we conduct a comprehensive comparison study of eight state-of-the-art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that graph-based visual saliency model and Itti model are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model.

A No Reference Image Quality Assessment Metric Based on Visual Perception

Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.

Land Surface Water Mapping Using Multi-Scale Level Sets and a Visual Saliency Model from SAR Images

Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to overcome the lack of an operational solution for automatically, rapidly and reliably extracting water from large-area and fine spatial resolution Synthetic Aperture Radar (SAR) images. This paper has two main contributions, as follows: (1) The method integrated the advantages of both level sets and the visual saliency model. First, the visual saliency map was applied to detect the suspected water regions (SWR), and then the level set method only needed to be applied to the SWR regions to accurately extract the water bodies, thereby yielding a simultaneous reduction in time cost and increase in accuracy; (2) In order to make the classical Itti model more suitable for extracting water in SAR imagery, an improved texture weighted with the Itti model (TW-Itti) is employed to detect those suspected water regions, which take into account texture features generated by the Gray Level Co-occurrence Matrix (GLCM) algorithm, Furthermore, a novel calculation method for center-surround differences was merged into this model. The proposed method was tested on both Radarsat-2 and TerraSAR-X images, and experiments demonstrated the effectiveness of the proposed method, the overall accuracy of water mapping is 98.48% and the Kappa coefficient is 0.856.

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients.

Saliency Mapping Enhanced by Structure Tensor.

We propose a novel efficient algorithm for computing visual saliency, which is based on the computation architecture of Itti model. As one of well-known bottom-up visual saliency models, Itti method evaluates three low-level features, color, intensity, and orientation, and then generates multiscale activation maps. Finally, a saliency map is aggregated with multiscale fusion. In our method, the orientation feature is replaced by edge and corner features extracted by a linear structure tensor. Following it, these features are used to generate contour activation map, and then all activation maps are directly combined into a saliency map. Compared to Itti method, our method is more computationally efficient because structure tensor is more computationally efficient than Gabor filter that is used to compute the orientation feature and our aggregation is a direct method instead of the multiscale operator. Experiments on Bruce's dataset show that our method is a strong contender for the state of the art.

Visual Saliency Models for Text Detection in Real World.

This paper evaluates the degree of saliency of texts in natural scenes using visual saliency models. A large scale scene image database with pixel level ground truth is created for this purpose. Using this scene image database and five state-of-the-art models, visual saliency maps that represent the degree of saliency of the objects are calculated. The receiver operating characteristic curve is employed in order to evaluate the saliency of scene texts, which is calculated by visual saliency models. A visualization of the distribution of scene texts and non-texts in the space constructed by three kinds of saliency maps, which are calculated using Itti's visual saliency model with intensity, color and orientation features, is given. This visualization of distribution indicates that text characters are more salient than their non-text neighbors, and can be captured from the background. Therefore, scene texts can be extracted from the scene images. With this in mind, a new visual saliency architecture, named hierarchical visual saliency model, is proposed. Hierarchical visual saliency model is based on Itti's model and consists of two stages. In the first stage, Itti's model is used to calculate the saliency map, and Otsu's global thresholding algorithm is applied to extract the salient region that we are interested in. In the second stage, Itti's model is applied to the salient region to calculate the final saliency map. An experimental evaluation demonstrates that the proposed model outperforms Itti's model in terms of captured scene texts.

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach

The human visual system (HVS) possesses a remarkable ability of real-time complex scene analysis despite the limited neuronal hardware available for such tasks. The HVS successfully overcomes the problem of information bottleneck by selecting potential regions of interest and reducing the amount of data transmitted to high-level visual processing. On the other hand, many man-made systems are also confronted with the same problem yet fail to achieve satisfactory performance. Among these, the synthetic aperture radar-based automatic target recognition (SAR-ATR) system is a typical one, where the traditional detection algorithm employed is termed the constant false alarm rate (CFAR). It is known to exhibit a low probability of detection (PD) and consumes too much time. The visual attention model (VAM) is a computational model, which aims to imitate the HVS in predicting where humans will look. The application of VAM to the SAR-ATR system could thus help solve the problem of effective real-time processing of complex large amounts of data. In this paper, we propose a new vehicle target detection algorithm for SAR images based on the VAM. The algorithm modifies the well-known Itti model according to the requirements of target detection in SAR images. The modified Itti model locates salient regions in SAR images and following top-down processing reduces false alarms by using prior knowledge. Real SAR data are used to demonstrate the validity and effectiveness of the proposed algorithm, which is also benchmarked against the traditional CFAR algorithm. Simulation results show comparatively improved performance in terms of PD, number of false alarms and computing time.

Video quality assessment using visual attention computational models

A recent development in the area of image and video quality consists of trying to incorporate aspects of visual attention in the design of visual quality metrics, mostly using the assumption that visual distortions appearing in less salient areas might be less visible and, therefore, less annoying. This research area is still in its infancy and results obtained by different groups are not yet conclusive. Among the works that have reported some improvements, most use subjective saliency maps, i.e., saliency maps generated from eye-tracking data obtained experimentally. Other works address the image quality problem, not focusing on how to incorporate visual attention into video signals. We investigate the benefits of incorporating bottom-up video saliency maps (obtained using Itti's computational model) into video quality metrics. In particular, we com- pare the performance of four full-reference video quality metrics with their modified versions, which had saliency maps incorporated into the algorithm. Results show that the addition of video saliency maps improve the per- formance of most quality metrics tested, but the highest gains were obtained for the metrics that only took into consideration spatial degradations. © 2014 SPIE and IS&T (DOI: 10.1117/1.JEI.23.6.061107)

An Image Quality Assessment Approach Based on Saliency Map in Space Domain

This paper proposes a no-reference image assessment approach (IQA) based on saliency map in the space domain of the image. The saliency map of the image is extracted by Itti model at first. Next, the saliency-map weighted normalized image is used to get the histogram of the image, then the histogram is modeled by generalized gaussian distribution and the parameter of the generalized gaussian distribution is estimated by parameter estimating approach. Parameters of the generalized gaussian distribution are used as the feature vector for the training and testing image. The feature vectors of the testing image are fed to support vector regresion machine to evaluate the image quality score. Experimental results show that our approach outperforms the recent method of no-reference IQA.

Tag ranking based on salient region graph propagation

In this paper, the problem of tag ranking by propagating relevance over community-contributed images and their associated tags is explored. To rank the tags more accurately, we propose a novel tag ranking approach based on the salient region. Firstly, we extract the salient region sub-image based on Itti model, and then construct two graphs with the whole image and the salient region sub-image. Secondly, we use the graph-based model to propagate the relevance of labels. Finally, we calculate the relevance score according to the results of the relevance propagation on the two sparse graphs. And then the new order of tags is determined by the relevance of the tags from high to low. Compared to existing methods, the proposed method considers not only the relationship between the whole images, but also the relationship between the salient regions. Therefore, it enhances the accuracy of the tag ranking. Experimental results conducted on a real dataset demonstrate that the ranking result of the proposed approach is closer to the manual rank.

Novel biological visual attention mechanism via Gaussian harmony search

Most of the visual attention models are based on the concept of a two-dimensional saliency map, which encodes the conspicuity of the object in the visual scene. The visual attention model proposed by Laurent Itti is used in this work. In Itti's model, the saliency map is calculated via combining the information across several modalities, including color, intensity, and orientation. In this work, we propose a pre-training process to select the weightings used in the combining of feature maps to make the target more conspicuity in the saliency map. Harmony search (HS) algorithm is used in the pre-training process to obtain the weightings. HS is a new heuristic algorithm, which mimics the improvisation of music players. Its performance has been verified by many benchmark problems. We modify the pitch adjustment process of the original HS to improve the optimization performance and accelerate the convergence rate. The modified algorithm is named Gaussian harmony search (GHS).

Applications and New Research of Visual Saliency and Attention

Drawing on the suvey of visual attention degree and its significance in psychology and physiology , in recent years, researchers have proposed a lot of visual attention model and algorithms, such as Itti model and many saliency detection algorithms. And in recent years, the researchers applied the visual attention of this technology in a lot of directions, such as a significant regional shifts and visual tracing detection model based on network loss, for video quality evaluation. This paper summarizes the various algorithms and its application of visual attention and its significance.