Histogram Pyramid Research Articles

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

Histograms of sequences: a novel representation for human interaction recognition

This study presents a novel representation based on hierarchical histogram of local feature sequences for human interaction recognition. The authors’ method basically combines the power of discriminative sequence mining and histogram representation for the effective recognition of human interactions. Our framework involves extracting visual features from the videos first, and then mining sequences of the visual features that occur consequently in space and time. After the mining step, we represent each video with a histogram pyramid of such sequences. We also propose to use soft clustering in the visual word construction step, such that more information-rich histograms can be obtained. The authors’ experimental results on challenging human interaction recognition data sets indicate that the proposed algorithm performs on par with the state-of-the-art methods.

Training-less color object recognition for autonomous robotics

This paper looks at the challenge of object recognition from the perspective of achieving the final goals of practical real-world autonomous robot-vision applications; that of identifying a target of interest in the robot’s field of view and properly localizing its position in preparation for the higher-level goal of tracking and navigation. A unified framework is introduced that combines a multidimensional feature histogram approach with a multiscale pyramid approach for training-less color object recognition and localization, with direct application to autonomous robotic agents. This framework addresses the high computational cost associated with multidimensional processing by deriving the Multidimensional “Laplacian Feature Histogram Pyramid”, a novel approach to a unified multidimensional-multiscale histogram representation. Furthermore, a Taylor series formulation is employed to combine the multiscale levels of the multidimensional Laplacian feature histogram pyramid into one efficient multidimensional-multiscale “Laplacian-Taylor Feature Histogram” for rapid object recognition and localization. The paper describes the criteria for target detection and localization by autonomous robots and how this newly developed framework fits these needs. Comparative results demonstrate the robustness of this recognition framework to noise and localization of target objects in cluttered scenes.

Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval

Exploiting local feature shape has made geometry indexing possible, but at a high cost of index space, while a sequential spatial verification and re-ranking stage is still indispensable for large scale image retrieval. In this work we investigate an accelerated approach for the latter problem. We develop a simple spatial matching model inspired by Hough voting in the transformation space, where votes arise from single feature correspondences. Using a histogram pyramid, we effectively compute pair-wise affinities of correspondences without ever enumerating all pairs. Our Hough pyramid matching algorithm is linear in the number of correspondences and allows for multiple matching surfaces or non-rigid objects under one-to-one mapping. We achieve re-ranking one order of magnitude more images at the same query time with superior performance compared to state of the art methods, while requiring the same index space. We show that soft assignment is compatible with this matching scheme, preserving one-to-one mapping and further increasing performance.

Speaker recognition using pyramid match kernel based support vector machines

Gaussian mixture model (GMM) based approaches have been commonly used for speaker recognition tasks. Methods for estimation of parameters of GMMs include the expectation-maximization method which is a non-discriminative learning based method. Discriminative classifier based approaches to speaker recognition include support vector machine (SVM) based classifiers using dynamic kernels such as generalized linear discriminant sequence kernel, probabilistic sequence kernel, GMM supervector kernel, GMM-UBM mean interval kernel (GUMI) and intermediate matching kernel. Recently, the pyramid match kernel (PMK) using grids in the feature space as histogram bins and vocabulary-guided PMK (VGPMK) using clusters in the feature space as histogram bins have been proposed for recognition of objects in an image represented as a set of local feature vectors. In PMK, a set of feature vectors is mapped onto a multi-resolution histogram pyramid. The kernel is computed between a pair of examples by comparing the pyramids using a weighted histogram intersection function at each level of pyramid. We propose to use the PMK-based SVM classifier for speaker identification and verification from the speech signal of an utterance represented as a set of local feature vectors. The main issue in building the PMK-based SVM classifier is construction of a pyramid of histograms. We first propose to form hard clusters, using k-means clustering method, with increasing number of clusters at different levels of pyramid to design the codebook-based PMK (CBPMK). Then we propose the GMM-based PMK (GMMPMK) that uses soft clustering. We compare the performance of the GMM-based approaches, and the PMK and other dynamic kernel SVM-based approaches to speaker identification and verification. The 2002 and 2003 NIST speaker recognition corpora are used in evaluation of different approaches to speaker identification and verification. Results of our studies show that the dynamic kernel SVM-based approaches give a significantly better performance than the state-of-the-art GMM-based approaches. For speaker recognition task, the GMMPMK-based SVM gives a performance that is better than that of SVMs using many other dynamic kernels and comparable to that of SVMs using state-of-the-art dynamic kernel, GUMI kernel. The storage requirements of the GMMPMK-based SVMs are less than that of SVMs using any other dynamic kernel.

High‐speed Marching Cubes using HistoPyramids

Abstract We present an implementation approach for Marching Cubes (MC) on graphics hardware for OpenGL 2.0 or comparable graphics APIs. It currently outperforms all other known graphics processing units (GPU)‐based iso‐surface extraction algorithms in direct rendering for sparse or large volumes, even those using the recently introduced geometry shader (GS) capabilites. To achieve this, we outfit the Histogram Pyramid (HP) algorithm, previously only used in GPU data compaction, with the capability for arbitrary data expansion. After reformulation of MC as a data compaction and expansion process, the HP algorithm becomes the core of a highly efficient and interactive MC implementation. For graphics hardware lacking GSs, such as mobile GPUs, the concept of HP data expansion is easily generalized, opening new application domains in mobile visual computing. Further, to serve recent developments, we present how the HP can be implemented in the parallel programming language CUDA (compute unified device architecture), by using a novel 1D chunk/layer construction.

Hierarchical fast two‐dimensional entropic thresholding algorithm using a histogram pyramid

Entropic thresholding provides an alternative view to the de- sign rationale of conventional thresholding. Abutaleb proposed a second-order entropic thresholding approach to improve Pun's first-order entropic thresholding by introducing a 2-D gray-level histogram to take into account the spatial correlation. Abutaleb's (1989) method was fur- ther modified by Brink (1992). However, a major drawback of Abutaleb- Brink's method is very high computational cost. Recently, Chen et al. (1994) developed a fast efficient 2-D algorithm that reduces computa- tional complexity from O(L 4 )t o O ( L 8/3 ), where L is the total number of gray levels. A hierarchical fast 2-D entropic thresholding algorithm using a gray-level histogram pyramid is presented that can be viewed as a generalization of Chen et al.'s algorithm. The new algorithm consists of a 2-D gray-level histogram pyramid build-up procedure expanding Ab- utaleb's 2-D gray-level histogram to a histogram pyramid, and a thresh- olding process applying a modified version of Chen et al.'s algorithm to the histogram pyramid layer by layer from top to bottom. As a result, the computational complexity of Chen et al.'s algorithm can be further re- duced to the optimal complexity, O(L 2 ). The experiments show that the computer time of the new algorithm is only one tenth of that required for Chen et al.'s algorithm, which is a significant saving. © 1996 Society of Photo-Optical Instrumentation Engineers.

Identification and boundary extraction of blobs in complex imagery

Automated identification and boundary extraction of blobs in “real world” imagery is a difficult task because the boundaries are so irregular that there is often insufficient a priori information describing these boundaries and traditional methods fail. This paper has proposed a progressive segmentation approach and a boundary estimation method to identify the blobs and to yield an accurate description of its boundary. The multiresolution image processing technique is incorporated into the whole work. This work has been applied to the problem of identifying and extracting the boundaries of major vessels (e.g., the aorta) in Magnetic Resonance (MR) imagery and the results are satisfactory. A Laplacian of Gaussian (LOG) operator is utilized as a spot detector to locate the approximate position of the blob of interest. A subimage centered on this approximate position is extracted to eliminate unwanted portions of the image and facilitate further processing. A histogram pyramid is created for the subimage histogram for automated determination of the threshold in the noisy histogram. A shrink-expand operation is then employed to reduced noise and undesired structures in the subimage. The rough and irregular boundary of the blob of interest obtained by thresholding is reparameterized into polar coordinates to create a Fourier descriptor representation of the boundary. Then, the discrete Fourier transform is applied to the reparameterized 1-D discrete curve to permit appropriate smoothing, as required, in frequency space. Finally, the boundary estimation is completed by taking the inverse Fourier transform to reconstruct the boundary of interest.