Global feature integration based salient region detection

The visual salient regions detection is one of the fundamental problems in computer vision, so saliency estimation has become a valuable tool in image processing. In this paper, we propose a novel method to realize the calculation of saliency, using color contrast and connectivity prior (called CCP for short). There are three cues integrated to obtain high-quality map, including contrast, spatial distribution and high-level prior. We evaluate our approach on three standard benchmark datasets with other state-of-the-art approaches, the results show that the proposed method has the higher precision and recall, the final maps are more closed to the ground truth.

Salient region detection is to uniformly locate interest regions or objects in an image. It is a hot topic in computer vision, and has a wide range of applications like object recognition and segmentation. Although considerable progress has been made, salient region detection remains a challenging issue. In this paper we present a simple yet effective salient region detection approach by integrating spatial and background priors. By considering spatial priors which include spatial distribution of color similarity and center-bias, the background regions and foreground regions are extracted preliminarily. Based on the extracted background we use the background prior to suppress the background regions more sufficiently. Experimental results on public benchmark databases show that the proposed approach can effectively locate salient object regions with well-defined boundaries and suppress background regions.

In this article, we tackle the saliency detection task from an interesting perspective: we focus both on salient regions (or foreground) detection and nonsalient regions (or background) detection instead of only the foreground and propose a novel complementarity-aware attention network. It is a unified framework with two branches, namely, positive attention module (PAM) and negative attention module (NAM), for the foreground and background detection, respectively. More specifically, the PAM exploits a position self-attention mechanism to enhance the discriminant ability of feature representation, which can detect most of the salient object regions. Meanwhile, the NAM is designed to detect the background regions, aiming to pop out the missing object parts and details in the prediction map produced by the PAM. By fusing these two attention modules together, NAM can provide complementary cues to assist PAM for precise object detection. Furthermore, in order to capture more multiscale contextual information, we introduce a bidirectional structure with multisupervision to the proposed complementarity-aware attention module for performance improvement. Experiments on five benchmark datasets show that the proposed framework achieves comparable results compared with the state-of-the-art saliency detection methods.

Frequency-tuned saliency detection analyzes image saliency from the perspective of frequency domain and fully combines image segmentation method, which outputs well- defined boundaries of salient objects. However, the method ignores spatial relationships across image parts. This paper proposes an improved saliency detection method on the basis of the frequency-tuned method. In this method, we first segment the input image into regions and then analyze the image from the frequency domain. After that, we preprocess it using Gaussian filter to eliminate noise and coding artifacts. For each region, we can get saliency map in region-level based on region contrast. Finally, salient regions are selected by winner-take-all (WTA) neural network and Inhibition of Return(IOR) mechanism. The proposed salient region detection algorithm combines the virtues of frequency-tuning and region contrast. The experimental results show the feasibility and validity of this algorithm. Keywords-Image retrieval; Semantic gap; Saliency detection; Region-level saliency map; Visual attention

Finding most striking region in an image is known as salient region detection. This area is becoming an area of research in recent years due to its wide applicability in computer vision, robotics and data transmission. To find salient region various parameters like color, texture, location, semantics etc are used. In this paper, a method is proposed which uses the fact that image boundary is rarely touched by salient object and then poisson distribution is used to find the probability of each pixel being part of salient object. Thus, the saliency map is obtained by computing the absolute difference of pixel intensity with the mean of boundary pixels. No former training is essential for this approach. Experiments are performed to evaluate the performance of proposed approach on MSRA dataset and compared with 4 recent state-of-arts. Precision, recall and F-measure proves the consistency of proposed work.

Automatic detection of salient image regions is a useful tool with applications in intelligent camera control, virtual cinematography, video summarization and editing, evaluation of viewer preferences, and many others. This paper presents an effective method for detecting potentially salient foreground regions. Salient regions are identified by eigenvalue analysis of a graph Laplacian that is defined over the color similarity of image superpixels, under the assumption that the majority of pixels on the image boundary show non-salient background. In contrast to previous methods based on graph-cuts or graph partitioning, our method provides continuously-valued saliency estimates with complementary properties to recently proposed color contrast-based approaches. Moreover, exploiting discriminative properties of the Fiedler vector, we devise an SVM-based classifier that allows us to determine whether an image contains any salient objects at all, a problem that has been largely neglected in previous works. We also describe how the per-frame saliency detection can be extended to improve its spatiotemporal coherence when computed on video sequences. Extensive evaluation on several datasets demonstrates and validates the state-of-the-art performance of the proposed method.

Graph and hypergraph matching are fundamental problems in computer vision, which have been receiving a steadily growing attention in the last two decades due to the recent modern algorithms and models that are both fast and accurate. Graph and hypergraph matching are successful in tackling many real-world tasks that require 2D and 3D feature matching and alignment. Matching is a general problem in vision and graphs have always been quintessential for modeling information that has both local and longer range connections. Therefore, graph matching will continue to influence computer vision and robotics research, crossing over many different mathematical and computational models, including Bayesian networks, conditional and Markov random fields, spectral graph models, and, more recently, deep neural networks. While graph matching is limited to using pairwise relationships, hypergraph matching permits the use of relationships between sets of features of any order. Consequently, it carries the promise to make matching more robust to changes in scale, deformations, and outliers. In this chapter, we present several key ideas, computational models, and optimization methods that introduced in the literature the possibility to learn graph and hypergraph matching in a semi-supervised or even completely unsupervised fashion. We start by describing two methods for solving graph and hypergraph matching based on spectral matching and integer projected fixed point methods, introduced in Chap. 1. Then, based on the same intuition (also discussed in Chap. 1) that correct assignments form a strong cluster of agreements, we follow up by introducing the main approach to learning as it applies to graph and hypergraph matching, as well as the more general Maximum A Posteriori (MAP) inference problem in probabilistic graphical models (MRFs and CRFs). In the last part of the chapter, we demonstrate experimentally the effectiveness of the algorithms presented and, whenever possible, provide extensive comparisons to other top published methods. Moreover, we show that our learning approach is general and can significantly boost the performance of different state-of-the-art matching algorithms in the literature.

Which parts of an image evoke emotions in an observer? To answer this question, we introduce a novel problem in computer vision — predicting an Emotion Stimuli Map (ESM), which describes pixel-wise contribution to evoked emotions. Building a new image database, EmotionROI, as a benchmark for predicting the ESM, we find that the regions selected by saliency and objectness detection do not correctly predict the image regions which evoke emotion. Although objects represent important regions for evoking emotion, parts of the background are also important. Based on this fact, we propose using fully convolutional networks for predicting the ESM. Both qualitative and quantitative experimental results confirm that our method can predict the regions which evoke emotion better than both saliency and objectness detection.

Salient instance segmentation via subitizing and clustering

Selective Visual Attention Model (SVAM) plays an important role in region-based image retrieval. In this paper, a robust and accurate method for salient region detection is proposed which integrates SVAM and image segmentation. After that, the concept of salient region adjacency graphs (SRAGs) is introduced for image retrieval. The whole process consists of three levels. First in the pixel-level, the salient value of each pixel is calculated using an improved spatial-based attention model. Then in the region-level, the salient region detection method is presented. Furthermore, in the scene-level, salient region adjacency graphs (SRAGs) are introduced to represent the salient groups in the image, which take the salient regions as root nodes. Finally, the constructed SRAGs are used for image retrieval. Experiments show that the proposed method works well.KeywordsRoot NodeImage RetrievalSalient RegionImage Segmentation MethodAttention WindowThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this research, a unique colour image reconstruction method based on Compressive Sensing is presented, aimed at enhancing efficiency and quality. The proposed method incorporates deep learning techniques, adaptive measurement rate setting, and salient region detection. The process begins with the collection of an input RGB images, followed by pre-processing to convert them to the Y+ colour space., including optimized Faster R-CNN (OFRCNN), are employed to detect and segment salient regions in the Y-channel. An adaptive measurement rate setting using Deep Q-Network (DQN) dynamically adjusts measurement rates based on input data characteristics. To optimize the Y-channel compression, a Dual attention-based CS Network (CSNet) model is applied. Moreover, adaptive weighted reconstruction is introduced, utilizing salient region masks and a hybrid the Walrus Optimization Algorithm (WaOA) and mountain gazelle optimizer (MGO), called Hybrid Walrus with Mountain Gazelle Optimizer (HWMGO). Finally, the restored Y-channel is combined with the reconstructed U and V channels to reconstruct full-colour in the RGB colour space. Experimental evaluations on a diverse dataset demonstrate the effectiveness of the proposed algorithm in accurately reconstructing high-quality colour images from CS data. This research contributes to the advancement of colour image reconstruction techniques, offering a comprehensive and efficient framework for various applications.

Contour detection is a fundamental problem in computer vision. However, there is still a considerable disparity between detection results and actual contours. To detect object-level contours on the basis of comprehensive analysis of potential edges, we present a deep-learning-based approach with a conditional random fields (CRF) model. We obtain the initial edgemap with a VGGNet-based model, and establish a contour correlation graph (CCG) to describe the potential edges and their relationships. Then a CRF model is adopted to fulfill the optimization on the basis of the analysis of object-level contour characteristics and to predict the validity of candidate contours. The experiment results on various datasets demonstrate that the proposed method outperforms the existing methods by a noticeable margin.

Even though there have been great advancements in computer vision tasks, the development of human visual attention models is still not well investigated. In day-to-day life, one can find ample applications of saliency detection in image and video processing. This paper presents an efficient visual saliency detection model based on Ripplet transform, which aims at detecting the salient region and achieving higher Receiver Operating Characteristics (ROC). Initially the feature maps are obtained from Ripplet transform in different scales and different directions of the image. The global and local saliency maps are computed based on the global probability density distribution and feature distribution of local areas, which are combined together to get the final saliency map. Ripplet-transform-based visual saliency detection is the novel approach carried out in this paper. Experimental results indicate that the proposed method based on Ripplet transformation can give excellent performance in terms of precision, recall, F measure and Mean Absolute Error (MAE), and is compared with 10 state-of-the-art methods on five benchmark datasets.

Saliency region detection plays an important role in computer vision applications and related areas, such as human fixation, figure-ground separation, face detection, and image compression. Nevertheless, state-of-the-art methods can moderately detect the saliency regions of a psychological pattern dataset. Therefore, this paper proposes a spatial-frequency redistribution (SFR) method to improve the efficiency of the detection of the saliency regions. The proposed SFR method consists of two main procedures: (i) adaptive cosine image construction-and-implantation and (ii) saliency region detection using complete multiple-object-implanted images. The former procedure constructs an adaptive cosine image by using a cosine function based on an object structure and then individually implants it into each object detectable. This stage provides the first significant property, spatial redistribution, to the object implanted. The adaptive cosine image redistributes the objects for controllability. Then, the latter procedure transforms a complete multiple object-implanted image into the frequency domain. At this point, the second significant property, frequency redistribution, provides the simple technique for identifying and separating the target object from the unwanted objects and background. In this paper, these properties are referred to as spatial-frequency redistribution. This method was realized as a computer program, and then such program was tested with a psychological pattern dataset and a Thai text dataset. The experimental results, when compared with state-of-the-art methods, show that the proposed SFR method can achieve the clear detection of the attention region in the psychological pattern dataset. Moreover, the proposed method can achieve a F-value of 80.28% with a recall and precision of 76.32% and 85.23%, respectively, for Thai text localisation in the Thai text dataset.

No object is mysterious. The mystery is your eye. – Elizabeth Bowen Introduction There are two fundamentally different philosophies concerning understanding the brain. (1) Understand the brain first. If we can understand how the brain works, we can build smart machines. (2) Using any technique we can think of, make a smart machine. If we can accomplish that, it will give us some hints about how the brain works. This book is all about the second approach, although it draws from current understanding of biological computing. In this chapter, however, we define a few terms, introduce the greater localglobal problem, and then give a very brief introduction to the function of the mammalian brain. • (Section 1.2) From signal and systems perspective, we describe the differences between Computer Vision and some other closely related fields of studies, including, e.g., image processing and pattern recognition. • (Section 1.3) Since almost all problems in Computer Vision involve the issue of localness versus globalness, we briefly explain the “local-global” problem and the “consistency” principle used to solve this problem. • (Section 1.4) Computer Vision is deep-rooted in biological vision. Therefore, in this section, we discuss the biological motivation of Computer Vision and some amazing discoveries from the study of the human visual system. Some Definitions Computer Vision is the process whereby a machine, usually a digital computer, automatically processes an image and reports “what is in the image.” That is, it recognizes the content of the image. For example, the content may be a machined part, and the objective may be not only to locate the part but to inspect it as well. Students tend to get confused by other terms that often appear in the literature, such as Image Processing , Machine Vision , Image Understanding , and Pattern Recognition . We can divide the entire process of Image Processing into Low-Level Image Processing and High-Level Image Processing . If we interpret these processes from signal and systems perspective, it is more clear to describe their difference and similarity from the format of input/output of the system. When a Low-Level Image Processing system processes an input image, the output is still an image, but a somewhat different image. For example, it may be an image with noise removed, an image that does not take as much storage space as the input image, an image that is sharper than the input image, etc.