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Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space. However, there are many MIL problems that do not fit this formulation well, and hence cause traditional MIL algorithms, which focus...
Automatic age classification from human faces is a challenging task which has recently attained an increasing attention. Most of the proposed approaches have however been mainly concerning controlled settings. In this paper, we propose a novel method for age classification in unconstrained conditions and provide extensive performance evaluation on benchmark datasets with standard protocols, thus allowing...
In this paper a novel 2D shape recognition approach is proposed. The main idea is to exploit in this context the huge amount of work carried out by bioinformati-cians in the biological sequence analysis research field. In the proposed approach, we encode shapes as biological sequences, employing standard and well established sequence alignment tools to devise a similarity score, finally used in a...
In cluttered environments the overhead view is often preferred because looking down can afford better visibility and coverage. However detecting people in this or any other extreme view can be challenging as there is a significant variation in a person's appearances depending only on their position in the picture. The Histogram of Oriented Gradient (HOG) algorithm, a standard algorithm for pedestrian...
Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick cooccurrence matrices and local binary patterns...
In this paper we present a hybrid generative-discriminative approach for image categorization in real-world images, based on Latent Dirichlet Allocation and SVM classifiers. We use SVMs with non-linear kernels on different visual features in a multiple kernel combination framework. A major contribution of our work is also the introduction of a novel dataset, called MICC-Flickr101, based on the popular...
Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then we determine whether the two cells represent the same cell over time by training...
Environment illumination is a key to achieving a realistic visualization of material appearance. One way to achieve such an illumination is an approximation by rendering of the material surface lit by a finite set of point light sources. In this paper we employed visual psychophysics to identify a minimal number of point light sources approximating realistic illumination. Furthermore, we analyzed...
Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much attention in computer vision. Recently, a Grassmann manifold-based domain adaptation algorithm that models the domain shift using intermediate subspaces along the geodesic connecting the source and target domains was presented in [6]. We build upon this work and propose replacing...
We present a new method for the detection of multiple homographies in image pairs. Our aim is to show that we can approach the optimal solution in a short time using an approach based on the well-known RANSAC algorithm. Given feature correspondences between two similar images, our algorithm iteratively generates homography hypotheses using a suitable sampling, optimizes the promising hypotheses and...
In recent years, various methods have been proposed for recovering depth blur and motion blur by coding camera optics, such as aperture and exposure. However, these methods are limited to deblurring just a single type of blur, such as depth blur or motion blur. In this paper, we propose a method, which enables us to deblur the depth blur and the motion blur simultaneously by coding image capture both...
Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From...
Video-based biometric systems are becoming feasible thanks to advancement in both algorithms and computation platforms. Such systems have many advantages: improved robustness to spoof attack, performance gain thanks to variance reduction, and increased data quality/resolution, among others. We investigate a discriminative video-based score-level fusion mechanism, which enables an existing biometric...
Several citizen service databases such as, police, national citizen identity, passport and vehicle registration, store both biographical and biometric information containing huge number of records. Achieving scalability and high accuracy for a 1:N person identification task on these databases is a huge challenge. In this work, we propose to use complementary information present in the biographical...
Pedestrian detection is a key problem in many computer vision applications, especially in surveillance and security systems. To this end, information integration from different imaging modalities, such as thermal infrared and visible spectrum, can significantly improve the detection rate in respect to mono-modal strategies. For this reason, an effective fusion scheme is necessary to combine the information...
In the field of computer vision, pyramid matching by minimization has gained increasing popularity. This paper points out and discusses an inherent anomaly in pyramid matching by minimization that can affect the performance of classification approaches based on this type of matching. As a solution, a new multiresolution measure, called Manhattan-Pyramid Distance (MPD), is proposed. Systematic evaluations...
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) several regularization techniques, (3) weighted extensions, (4) some clustering methods,...
Microsoft's Kinect as a recent 3D sensor has attracted considerable research attention in the fields of computer vision and pattern recognition. But its depth image suffers from the problem of poor accuracy caused by invalid pixels, noise and unmatched edges. In this paper, an efficient approach is proposed to improve the quality of Kinect's depth image. Using its corresponding color image, the pixels...
Design of video storyboards has emerged as a popular research area in the multimedia community. Different pattern clustering techniques are applied to extract the key frames from a video sequence to form a storyboard. In this paper, we propose an automatic method for the selection of key frames of a video sequence using Delaunay graphs. We prune certain edges from the Delaunay graph using an iterative...
This paper presents a renewed image annotation baseline method under the nearest neighbor tag transfer framework. Two key problems are considered in this paper: (1) which images are determined as the neighbors; (2) how their keywords are transferred. Firstly, a soft neighbor selection scheme is designed by image embedding technique, with which we can provide more power to the crucial neighbors in...
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