Hierarchical Image Research Articles

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.

Hierarchical Image Classification on Bayesian Cascade Neural Learning

Hierarchical Image Classification on Bayesian Cascade Neural Learning

Hierarchical segmentation from a non-increasing edge observation attribute

Hierarchical image segmentation provides region-oriented scale-spaces: sets of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph-based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. It computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should be merged according to the dissimilarity. We provide an explicit definition of the (edge-) observation attribute and Boolean criterion which are at the basis of this method and show that they are not increasing. Then, we propose an algorithm to compute all the scales for which the criterion holds true. Finally, we propose new methods to regularize the observation attribute and criterion and to set up the observation scale value of each edge of a graph, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy. Assessments on Pascal VOC 2010 and 2012 show that these strategies lead to better segmentation results than the ones obtained with the original HGB method.

Variational shape prior segmentation with an initial curve based on image registration technique

In general images, it is practically hard to distinguish only the desired object using the conventional image segmentation methods. In many cases, we can segment the desired object by using the shape information of the object in addition to the standard image segmentation. Chan and Zhu's model is not robust to the intensity changes of objects. In this paper, we propose a novel model for the shape prior segmentation that produces robust results using the hierarchical image segmentation and an attraction term. Moreover, we adopt an image registration technique and a multi-region image segmentation to get an initial for a given shape prior. Finally, we consider the free-form deformation in obtaining the shape function from the reference shape prior for real-world images. Numerical experiments demonstrate the results independent of intensities of objects and the location of the reference shape prior. All numerical calculations are automatic and progress without any user input.

Distributed Component Forests in 2-D: Hierarchical Image Representations Suitable for Tera-Scale Images

The standard representations known as component trees, used in morphological connected attribute filtering and multi-scale analysis, are unsuitable for cases in which either the image itself or the tree do not fit in the memory of a single compute node. Recently, a new structure has been developed which consists of a collection of modified component trees, one for each image tile. It has to-date only been applied to fairly simple image filtering based on area. In this paper, we explore other applications of these distributed component forests, in particular to multi-scale analysis such as pattern spectra, and morphological attribute profiles and multi-scale leveling segmentations.

Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity

Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.

Connected filters on generalized shape-Spaces

Classical hierarchical image representations and connected filters work on sets of connected components (CC). These approaches can be defective to describe the relations between disjoint objects or partitions of images. In practice, objects can be made of several connected components in images (due to occlusions for example), therefore it can be interesting to be able to take into account the relationship between these components to be able to detect the whole object. In Mathematical Morphology, second-generation connectivity (SGC) and tree-based shape-spaces study this relation between the connected components of an image. However, they have limitations. For this reason, we propose in this paper an extension of the usual shape-space paradigm into what we call a Generalized Shape-Space (GSS). This new paradigm allows us to analyze any graph of connected components hierarchically and to filter them thanks to connected operators.

Phase retrieval algorithm for optical information security**Project supported by the National Natural Science Foundation of China (Grant Nos. 61775121, 61605165, 61307003, 61405122, and 11574311), the Key Research and Development Program of Shandong Province, China (Grant No. 2018GGX101002), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2019QF006), and the Fundamental Research Funds of Shandong University, China (Grant No. 2015GN031).

As a typical technology for optical encryption, phase retrieval algorithms have been widely used in optical information encryption and authentication systems. This paper presents three applications of two-dimensional (2D) phase retrieval for optical encryption and authentication: first, a hierarchical image encryption system, by which multiple images can be hidden into cascaded multiple phase masks; second, a multilevel image authentication system, which combines (t, n) threshold secret sharing (both t and n are positive integers, and ) and phase retrieval, and provides both high-level and low-level authentication; and finally, a hierarchical multilevel authentication system that combines the secret sharing scheme based on basic vector operations and the phase retrieval, by which more certification images can be encoded into multiple cascaded phase masks of different hierarchical levels. These three phase retrieval algorithms can effectively illustrate phase-retrieval-based optical information security. The principles and features of each phase-retrieval-based optical security method are analyzed and discussed. It is hoped that this review will illustrate the current development of phase retrieval algorithms for optical information security and will also shed light on the future development of phase retrieval algorithms for optical information security.

Hierarchical secret image sharing using phase modulation based on the Fresnel transform

ABSTRACTIn idealized secret image sharing (SIS), all of the shared images (shadows) play the same role, but in real life, the shared images are often hierarchical. This paper proposes a hierarchical secret image sharing (HSIS) method by means of optical imaging. To implement the scheme, a generalized single-phase modulation algorithm is proposed in Fresnel transform domain. Its features that multiple secret images can be recovered through combination of some shared images though each participant only holds one share of images. Theoretically, the secret images are divided into multiple phase encodings (shared images) by phase modulation, each secret image can be directly captured by the intensity detector when illuminating some of the shared images orderly displayed with parallel light. Experimental simulation shows that the shared images and the restored secret images do not have any size distortion, and further verifies the feasibility of the proposed scheme.

Multi-task cascade deep convolutional neural networks for large-scale commodity recognition

In recent years, deep convolutional neural network have achieved remarkable performance in object detection and image classification. However, there are still some practical challenges in large-scale image recognition tasks. To be specific, the visual separability between different object categories is extremely uneven, and some categories have strong inter-class similarities. Existing CNN networks are trained as flat n-way classifiers, which is usually not sufficient to meet the challenges. Hence, we propose a framework: multi-task cascade deep convolutional neural network (MTCD-CNN), which contains two phases: object detection and hierarchical image classification, for large-scale commodity recognition. First, the object detection framework is utilized to locate and crop the areas that may contain objects. Then, hierarchical spectrum clustering is adopted to construct a category and a tree-like image classification model. During the testing phase, the indistinguishable objects are classified from coarse to fine by searching the path of the category tree. The proposed hierarchical image classification method provides an insight into the data by identifying the group of classes that are hard to classify and require more attention when compared to others. Through extensive experiments and comparative analyses of commodity detection in supermarkets and stores of Jinzhou city, the performance of MTCD-CNN has proved to be superior to other advanced methods, indicating that our proposed method has effectively solved the problem of excessive similarity of confusingly similar categories.

Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction

A plethora of algorithms have been defined for foreground segmentation, a fundamental stage for many computer vision applications. In this paper, we propose a post-processing framework to improve the foreground segmentation performance of background subtraction algorithms. We define a hierarchical framework for extending segmented foreground pixels to undetected foreground object areas and for removing erroneously segmented foreground. First, we create a motion-aware hierarchical image segmentation of each frame that prevents merging foreground and background image regions. Then, we estimate the quality of the foreground mask through the fitness of the binary regions in the mask and the hierarchy of segmented regions. Finally, the improved foreground mask is obtained as an optimal labeling by jointly exploiting foreground quality and spatial color relations in a pixel-wise fully connected conditional random field. Experiments are conducted over four large and heterogeneous data sets with varied challenges (CDNET2014, LASIESTA, SABS, and BMC) demonstrating the capability of the proposed framework to improve background subtraction results.

A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

Deep learning (DL) is a hot topic in current pattern recognition and machine learning. DL has unprecedented potential to solve many complex machine learning problems and is clearly attractive in the framework of mobile devices. The availability of powerful pattern recognition tools creates tremendous opportunities for next-generation smart applications. A convolutional neural network (CNN) enables data-driven learning and extraction of highly representative, hierarchical image features from appropriate training data. However, for some data sets, the CNN classification method needs adjustments in its structure and parameters. Mobile computing has certain requirements for running time and network weight of the neural network. In this paper, we first design an image processing module for a mobile device based on the characteristics of a CNN. Then, we describe how to use the mobile to collect data, process the data, and construct the data set. Finally, considering the computing environment and data characteristics of mobile devices, we propose a lightweight network structure for optical character recognition (OCR) on specific data sets. The proposed method using a CNN has been validated by comparison with the results of existing methods, used for optical character recognition.

GRATE: A framework and software for GRaph based Analysis of Transmission Electron Microscopy images of polymer films

Organic electronics (electronic devices made from polymers or similar organic compounds) are an important class of thin film devices that exhibit flexibility, stretchability, softness and compatibility with biological systems, thus making them exceedingly attractive for bioelectronics and healthcare applications. Performance of these devices is highly correlated with the microstructure within the thin film. In other words, quantitative characterization of the microstructure is critical for understanding device performance. While several microscopy based imaging techniques – including Transmission Electron Microscopy (TEM) – are currently available to visualize the microstructure, analyzing the resulting data has remained predominantly manual. This makes extracting microstructural information from microscopic images subjective, and slow. We introduce a mathematical framework and associated software, GRATE, that integrates concepts from graph theory, image analysis and hierarchical (quad-tree) image decomposition to automatically extract a suite of microstructural features from TEM image data. Both local traits like lattice spacing, orientation and size measures as well as global traits like number and size distribution of crystallites are efficiently extracted. The software is modular, extensible and is packaged into a simple graphical user interface that can reduce time of adoption by practitioners.

Image quality wheel

We have collected a large dataset of subjective image quality “*nesses,” such as sharpness or colorfulness. The dataset comes from seven studies and contains 39,415 quotations from 146 observers who have evaluated 62 scenes either in print images or on display. We analyzed the subjective evaluations and formed a hierarchical image quality attribute lexicon for *nesses, which is visualized as image quality wheel (IQ-Wheel). Similar wheel diagrams for attributes have become industry standards in other sensory experience fields such as flavor and fragrance sciences. The IQ-Wheel contains the frequency information of 68 attributes relating to image quality. Only 20% of the attributes were positive, which agrees with previous findings showing a preference for negative attributes in image quality evaluation. Our results also show that excluding physical attributes of paper gloss, observers then use similar terminology when evaluating images with printed images or images viewed on a display. IQ-Wheel can be used to guide the selection of scenes and distortions when designing subjective experimental setups and creating image databases.

Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

ABSTRACTFocusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.

Hierarchical Image Segmentation Ensemble for Objectness in RGB-D Images

Objectness has recently become a standard step in many computer vision tasks. Among various techniques, those based on hierarchical image segmentation play a fundamental role for developments in new data modalities. In this paper, we address the problem of objectness in RGB-D images and propose a novel and effective approach, namely, hierarchical image segmentation ensemble (HISE). Different from existing image segmentation based methods that generate object segments or proposals largely by heuristics or empirical rules, HISE learns superpixel mergings with a hierarchical tree-structured ensemble, where individual merging models of the ensemble are formed by traversing different paths of the tree, and where both the merging accuracy and proposal diversity are emphasized. Furthermore, we use efficient feature measurements that support easy integration of additional clues. Extensive experiments conducted on the benchmark NYU-v2 RGB-D and SUN RGB-D data sets show the competency of our proposed method.

Shape-Based Analysis on Component-Graphs for Multivalued Image Processing

Abstract Connected operators based on hierarchical image models have been increasingly considered for the design of efficient image segmentation and filtering tools in various application fields. Among hierarchical image models, component-trees represent the structure of grey-level images by considering their nested binary level-sets obtained from successive thresholds. Recently, a new notion of component-graph was introduced to extend the component-tree to any grey-level or multivalued images. The notion of shaping was also introduced as a way to improve the anti-extensive filtering by considering a two-layer component-tree for grey-level image processing. In this article, we study how component-graphs (that extend the component-tree from a spectral point of view) and shapings (that extend the component-tree from a conceptual point of view) can be associated for the effective processing of multivalued images. We provide structural and algorithmic developments. Although the contributions of this article are theoretical and methodological, we also provide two illustration examples that qualitatively emphasize the potential use and usefulness of the proposed paradigms for image analysis purposes.

A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images.

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy is integrated into the image matting model for blood vessel segmentation. Normally the matting models require a user specified trimap, which separates the input image into three regions: the foreground, background and unknown regions. However, creating a user specified trimap is laborious for vessel segmentation tasks. In this paper, we propose a method that first generates trimap automatically by utilizing region features of blood vessels, then applies a hierarchical image matting model to extract the vessel pixels from the unknown regions. The proposed method has low calculation time and outperforms many other state-of-art supervised and unsupervised methods. It achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE DB1, respectively.

Model selection and application to high-dimensional count data clustering

EDCM, the Exponential-family approximation to the Dirichlet Compound Multinomial (DCM), proposed by Elkan (2006), is an efficient statistical model for high-dimensional and sparse count data. EDCM models take into account the burstiness phenomenon correctly while being many times faster than DCM. This work proposes the use of Minimum Message Length (MML) criterion for determining the number of components that best describes the data with a finite EDCM mixture model. Parameters estimation is based on the previously proposed Deterministic Annealing Expectation-Maximization (DAEM) approach. The validation of the proposed unsupervised algorithm involves different real applications: text document modeling, topic novelty detection and hierarchical image clustering. A comparison with results obtained for other information-theory based selection criteria is provided.

Neural Signatures of the Configural Superiority Effect and Fundamental Emergent Features in Human Vision

The concepts of grouping, emergence, and superadditivity (when a whole is qualitatively different from the sum of its parts) are critical in Gestalt psychology and essential to properly understand the information processing mechanisms underlying visual perception. However, very little is known about the neural processes behind these phenomena (particularly in terms of their generality vs. specificity and their time-course). Here, we used the configural superiority effect as a way to define “emergence” and “emergent features” operationally, employing an approach that can isolate different emergent features and compare them on a common scale. By assessing well-established event related potentials in a HD-EEG system, we found that the critical processes behind configural superiority and superadditive Gestalt phenomena are present in the window between 100 and 200 ms after stimulus onset and that these effects seem to be driven by specific attentional selection mechanisms. Also, some emergent features seem to be differentially processed in different brain hemispheres. These results shed new light on the issues of the generality vs. specificity of the neural correlates of different Gestalt principles, the hemispheric asymmetries in the processing of hierarchical image structure and the role of the N1 ERP component in reflecting feature selective mechanisms.