Region-based Image Representation Research Articles

RGTransformer: Region-Graph Transformer for Image Representation and Few-Shot Classification

The goal of few-shot image classification is to learn a classifier that can be well generalized to the unseen classes with a few available labeled samples. One major challenge for few-shot learning is how to conduct effective image representation for support and query images. Recently, local region-based image representation and metric learning approaches have been demonstrated effectively for few-shot classification problem. However, existing approaches generally conduct representations of image regions individually which thus lack of considering the rich spatial/structural relationships among image regions. In this paper, we propose to bridge the individual regions and exploit the structural contexts among regions via a novel Region-Graph Transformer (RGTransformer). In RGTransformer, each region aggregates the information from its neighboring regions and thus can obtain context-aware feature representations for regions. Using the proposed RGTransformer, we propose an effective metric learning model for few-shot image classification. We evaluate the proposed method on four benchmark datasets and experimental results demonstrate the effectiveness and advantages of the proposed RGTransformer.

Parallel implementation of the Image Block Representation using OpenMP

Herein, a parallel implementation in OpenMP of the Image Block Representation (IBR) for binary imagesis investigated. The IBR is a region-based image representation scheme that represents the binary image as a set of non-overlapping rectangular areas with object level, called blocks. The IBR permits the execution of operations on image areas instead of image points and therefore leads to a substantial reduction of the required computational complexity. The experimental and the analytically derived results from parallel implementation in OpenMP, on a multicore computer, proved that a very good overall performance can be achieved.

Region-Based Retrieval of Remote Sensing Images Using an Unsupervised Graph-Theoretic Approach

This letter introduces a novel unsupervised graph-theoretic approach in the framework of region-based retrieval of remote sensing (RS) images. The proposed approach is characterized by two main steps: (1) modeling each image by a graph, which provides region-based image representation combining both local information and related spatial organization, and (2) retrieving the images in the archive that are most similar to the query image by evaluating graph-based similarities. In the first step, each image is initially segmented into distinct regions and then modeled by an attributed relational graph, where nodes and edges represent region characteristics and their spatial relationships, respectively. In the second step, a novel inexact graph matching strategy, which jointly exploits a subgraph isomorphism algorithm and a spectral graph embedding technique, is applied to match corresponding graphs and to retrieve images in the order of graph similarity. Experiments carried out on an archive of aerial images point out that the proposed approach significantly improves the retrieval performance compared to the state-of-the-art unsupervised RS image retrieval methods.(RS) images.

Monocular Depth Ordering Using T-Junctions and Convexity Occlusion Cues

This paper proposes a system that relates objects in an image using occlusion cues and arranges them according to depth. The system does not rely on a priori knowledge of the scene structure and focuses on detecting special points, such as T-junctions and highly convex contours, to infer the depth relationships between objects in the scene. The system makes extensive use of the binary partition tree as hierarchical region-based image representation jointly with a new approach for candidate T-junction estimation. Since some regions may not involve T-junctions, occlusion is also detected by examining convex shapes on region boundaries. Combining T-junctions and convexity leads to a system which only relies on low level depth cues and does not rely on semantic information. However, it shows a similar or better performance with the state-of-the-art while not assuming any type of scene. As an extension of the automatic depth ordering system, a semi-automatic approach is also proposed. If the user provides the depth order for a subset of regions in the image, the system is able to easily integrate this user information to the final depth order for the complete image. For some applications, user interaction can naturally be integrated, improving the quality of the automatically generated depth map.

Task-Specific Image Partitioning

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks.

The Image Retrieval and Relevance Feedback Methods Based on Region

Traditional image retrieval method based on global features can only extract low-level features, which are far from the semantics that human expects. So there is a huge gap between low-level features and high-level semantics. In order to overcome this gap, two approaches have been widely used: high-level semantic image representation and relevance feedback. The paper is based on region representation that comes closely to the semantics. Region-based image retrieval (RBIR) can effectively exclude the affection of backgrounds. The main work in the paper is summarized as follows: Firstly in order to solve the problems of image segment and similarity measure, an algorithm about detecting visual attended region with respect to image segment is proposed in the paper. This algorithm combined human visual attention model can effective detect the meaningful regions. And the paper proposes a similarity measure algorithm named Modified Integrated Region Matching (MIRM), which is more robust for over segmented images. Secondly this paper focuses on the applications of relevance feedback in RBIR. By studying three cases that may occur in relevance feedback respectively, this paper introduces some relevance feedback algorithms. Those are image coding and cluster based relevance feedback algorithm, Mapping Convergence (MC) based relevance feedback algorithm, Adaptive Boosting (AdaBoost) Support Vector Machines (SVM) based relevance feedback algorithm, Asymmetric Bagging SVM based relevance feedback algorithm and region representation based SVM relevance feedback algorithm. A large number of experimental results demonstrate that all the algorithms, both region based image representation and relevance feedback, proposed in this paper can improve the performance and efficiency of image retrieval.

A region-based image representation for spatial reasoning and similarity retrieval in image database systems

In this paper we propose a region-based shape representation image database. We extend Guojun's method for representing an object to a representation of any picture that contains one or more objects. This system contains some functions like spatial reasoning, visualization, browsing and similarity retrieval and so on. A new vector-based method for spatial reasoning is also proposed in this paper. In addition to the advantages of good precision of Guojun's model, which required lower storage requirement than the FD-based method, our method has other advantages, for example, the abilities to represent any picture that contains more than one object, to reconstruct pictures and spatial reasoning.

A Learning State-Space Model for Image Retrieval

This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.

Models for region-based image representation

Models for region-based image representation