Spatial-Frequency Redistribution-based Saliency Region Detection for Thai Text Localisation

Detection of salient region in an image is a crucial problem in many cognition and computer vision applications like object detection, adaptive image compression, automatic image cropping, video and image analysis. A part of an image is considered as salient, if the set of pixels under consideration protrudes from the rest, in terms of features such as color, contrast and local orientations. Generally, computational models for saliency assume that the image under observation is clean and fails to deal with visual disturbances. This paper presents a robust method for the detection of salient regions, using the multi-resolution dynamic mode decomposition (MRDMD approach). Effectiveness of the proffered method for the detection of salient region within clean and noisy images was examined and successfully verified for a wide range of noise strengths.

Global feature integration based salient region detection

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.

Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L₀ smoothing filter and principle component analysis (PCA) play important roles in our framework. The L₀ filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.

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.

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.

The goal of salient region detection is to compute a saliency map that highlights the regions of salient objects in a scene. Recently, this problem has received a lot of research interest owing to its usefulness in many computer vision applications, e.g. object detection, action recognition, and various image/video processing applications. Due to the emerging applications on mobile devices and large-scale datasets, a desirable salient region detection method should not only output high quality saliency maps, but should also be highly computationally efficient. In this chapter, we address both the quality and speed requirements for salient region detection.

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.

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.

Salient instance segmentation via subitizing and clustering

A salient region is the most distinctive part of the image that captures human's attention. Saliency detection is a fundamental characteristic of the human visual system. Finding computational models which are able to detect salient regions is a challenging task for image processing and computer vision applications. Salient regions of various sizes can be detected from different scales. Therefore, selecting the best scales is an important issue. In this paper, an efficient multi-scale method to find salient regions is proposed. In order to include more features in evaluating saliency of a pixel, feature maps are generated using components of both the RGB and YUV color spaces. These features are combined into quaternions. Detecting salient regions of different sizes is addressed by utilizing a scale space analysis. Salient regions are detected by convolving the image amplitude spectrum with a low-pass Gaussian kernel of multiple scales. To incorporate more meaningful information, more than one scale is considered based on entropy criterion. The final saliency map is generated by normalizing the weighted saliency maps of these scales. Experiments are conducted on a dataset of natural images to evaluate the performance of the proposed method. Results show that the proposed method provides larger values of area under receiver operating characteristics curve, precision, recall and F-measure, in comparison to some of the state-of-the-art methods.

Object detection problems also expect fast and accurate location of target objects. But most approaches following convolutional neural networks (CNN) framework generate a large number of redundant candidate regions, wasting a large percent of computation time for candidate classification. Human visual system is able to quickly find the parts or features belonging to objects of interest. Hence, it is believed that by detecting salient regions associated with key classification features for specific objects can quickly provide rough location of objects to be detected. A feature mining method by adding an enforcement learning block on top of CNN framework is proposed. Experiment results showed that the proposed method is able to generate accurate task specific salient regions both for localization and classification. This study attempts to make object localization and classification in one shot based on salient region detection and puts forward a new idea for key feature mining and utilization.

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.However, face detection is currently possible in limited conditions. In addition, there are some methods that can be used for gender recognition [3], face recognition [5] and facial expression recognition [2].Nonetheless, there has been very little research on how to combine these methods. There has also been quite little research on how to apply these methods in human-computer interaction (HCI).Finding sets of techniques that complement each other in a useful way is one research challenge. There are some applications that take advantage of one or two computer vision techniques. For example, Christian and Avery [1] have developed an information kiosk that uses computer vision to detect potential users from a distance. A similar kiosk has been developed by us in the University of Tampere [4]. There are also some games that use simple computer vision techniques for the interaction. However, there are very few applications that use several computer vision techniques together such as face detection, facial expression recognition and gender recognition. Overall, there has been very little effort in combining different techniques.In my research I develop computer vision methods and combine them, so that the combined method can detect face, recognize gender and facial expressions. After successfully combining the methods, it is easier to develop HCI applications that take advantage of computer vision. Applications that can be used by small group of people are my specific interest. These applications allow me to build adaptive user interfaces and analyze the use of computer vision techniques in improving human-computer interaction.

Visual saliency guided video compression algorithm