Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A model of saliency-based visual attention for rapid scene analysis

Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.

/pdf/frequency-tuned-salient-region-detection-4po18ylvds.pdf

Frequency-tuned salient region detection

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Zhang%20-%20Multi-Label%20Learning%20Algorithms%20-%202013.pdf

A Review On Multi-Label Learning Algorithms

http://pancamr.lecture.ub.ac.id/files/2012/02/A-survey-on-vision-based-human-action-recognition_Image-and-Vision-Computing_2010.pdf

A survey on vision-based human action recognition

Classification based on image sets has recently attracted great research interest as it holds more promise than single image based classification. In this paper, we propose an efficient and robust algorithm for image set classification. An image set is represented as a triplet: a number of image samples, their mean and an affine hull model. The affine hull model is used to account for unseen appearances in the form of affine combinations of sample images. We introduce a novel between-set distance called Sparse Approximated Nearest Point (SANP) distance. Unlike existing methods, the dissimilarity of two sets is measured as the distance between their nearest points, which can be sparsely approximated from the image samples of their respective set. Different from standard sparse modeling of a single image, this novel sparse formulation for the image set enforces sparsity on the sample coefficients rather than the model coefficients and jointly optimizes the nearest points as well as their sparse approximations. A convex formulation for searching the optimal SANP between two sets is proposed and the accelerated proximal gradient method is adapted to efficiently solve this optimization. Experimental evaluation was performed on the Honda, MoBo and Youtube datasets. Comparison with existing techniques shows that our method consistently achieves better results.

/pdf/sparse-approximated-nearest-points-for-image-set-3krpeueror.pdf

Sparse approximated nearest points for image set classification

We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and their affine hull model. The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP). SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set. This novel sparse formulation enforces sparsity on the sample coefficients and jointly optimizes the nearest points as well as their sparse approximations. Unlike standard sparse coding, the data to be sparsely approximated are not fixed. A convex formulation is proposed to find the optimal SANPs between two sets and the accelerated proximal gradient method is adapted to efficiently solve this optimization. We also derive the kernel extension of the SANP and propose an algorithm for dynamically tuning the RBF kernel parameter while matching each pair of image sets. Comprehensive experiments on the UCSD/Honda, CMU MoBo, and YouTube Celebrities face datasets show that our method consistently outperforms the state of the art.

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

We formulate the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph. The seed (labeled) nodes are first detected using Markov random walks performed on two different graphs that represent the image. While the global properties of the image are computed from the random walk on a complete graph, the local properties are computed from a sparse k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a locally compact object. A few background nodes and salient nodes are further identified based upon the random walk based hitting time to the most salient node. The salient nodes and the background nodes will constitute the labeled nodes. A new graph representation of the image that represents the saliency between nodes more accurately, the “pop-out graph” model, is computed further based upon the knowledge of the labeled salient and background nodes. A semisupervised learning technique is used to determine the labels of the unlabeled nodes by optimizing a smoothness objective label function on the newly created “pop-out graph” model along with some weighted soft constraints on the labeled nodes.

Random Walks on Graphs for Salient Object Detection in Images

One of the key challenges in human action recognition from video sequences is how to model an action sufficiently. Therefore, in this paper we propose a novel motion-based representation called Motion Context (MC), which is insensitive to the scale and direction of an action, by employing image representation techniques. A MC captures the distribution of the motion words (MWs) over relative locations in a local region of the motion image (MI) around a reference point and thus summarizes the local motion information in a rich 3D MC descriptor. In this way, any human action can be represented as a 3D descriptor by summing up all the MC descriptors of this action. For action recognition, we propose 4 different recognition configurations: MW+pLSA, MW+SVM, MC+w 3-pLSA (a new direct graphical model by extending pLSA), and MC+SVM. We test our approach on two human action video datasets from KTH and Weizmann Institute of Science (WIS) and our performances are quite promising. For the KTH dataset, the proposed MC representation achieves the highest performance using the proposed w 3-pLSA. For the WIS dataset, the best performance of the proposed MC is comparable to the state of the art.

/pdf/motion-context-a-new-representation-for-human-action-4yc9nzlovc.pdf

Motion Context: A New Representation for Human Action Recognition

We present a robust salient region detection framework based on the color and orientation distribution in images. The proposed framework consists of a color saliency framework and an orientation saliency framework. The color saliency framework detects salient regions based on the spatial distribution of the component colors in the image space and their remoteness in the color space. The dominant hues in the image are used to initialize an expectation-maximization (EM) algorithm to fit a Gaussian mixture model in the hue-saturation (H-S) space. The mixture of Gaussians framework in H-S space is used to compute the inter-cluster distance in the H-S domain as well as the relative spread among the corresponding colors in the spatial domain. Orientation saliency framework detects salient regions in images based on the global and local behavior of different orientations in the image. The oriented spectral information from the Fourier transform of the local patches in the image is used to obtain the local orientation histogram of the image. Salient regions are further detected by identifying spatially confined orientations and with the local patches that possess high orientation entropy contrast. The final saliency map is selected as either color saliency map or orientation saliency map by automatically identifying which of the maps leads to the correct identification of the salient region. The experiments are carried out on a large image database annotated with ldquoground-truthrdquo salient regions, provided by Microsoft Research Asia, which enables us to conduct robust objective level comparisons with other salient region detection algorithms.

Yiqun Hu

Papers

Sparse approximated nearest points for image set classification

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

Random Walks on Graphs for Salient Object Detection in Images

Motion Context: A New Representation for Human Action Recognition

Salient Region Detection by Modeling Distributions of Color and Orientation