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In this paper, we address the problem of multi-instance multi-label learning (MIML) where each example is associated with not only multiple instances but also multiple class labels. In our novel approach, given an MIML example, each instance in the example is only associated with a single label and the label set of the example is the aggregation of all instance labels. Many real-world tasks such as...
Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has to rule out cancer for each...
A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental learning strategy based on a dynamic particle swarm optimization (DPSO) algorithm allows to produce heterogeneous ensembles of classifiers for video-based face recognition. This strategy is applied to an adaptive classification...
In this paper, a supervised learning strategy based on a Multi-Objective Particle Swarm Optimization (MOPSO) is introduced for ARTMAP neural networks. It is based on the concept of neural network evolution in that particles of a MOPSO swarm (i.e., network solutions) seek to determine user-defined parameters and network (weights and architecture) such that generalisation error and network resources...
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated...
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation "group" between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories...
A noticeable amount of research has been focused on biometric fusion. A new area is looking at utilization of AdaBoost-type learning methods in biometric fusion domain. These methods rely on an idea that by selecting a variety of biometric classifiers the error rate can be reduced. This paper presents a new evolutionary algorithm based on the multi-objective genetic approach, which automatically preserves...
We consider the problem of classification of multiple observations of the same object, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all unlabelled examples belong to the same unknown class. We propose a low complexity solution that is able to exploit the properties of the data manifold with a graph-based algorithm. It results into...
Ranking large scale image and video collections usually expects higher accuracy on top ranked data, while tolerates lower accuracy on bottom ranked ones. In view of this, we propose a rank learning algorithm, called Imbalanced RankBoost, which merges RankBoost and iterative thresholding into a unified loss optimization framework. The proposed approach provides a more efficient ranking process by iteratively...
This paper reports our preliminary study that aims to examine the effectiveness of training methods for land cover classification by artificial neural networks. We consider three training methods, namely, the gradient descent method, the conjugate gradient method, and the Quasi-Newton method. We apply these methods to derive land cover information from a Landsat Enhanced Thematic Mapper Plus (ETM+)...
Recently a new classification assumption was proposed in [1]. It assumed that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The classification algorithm in [1] was based on the idea that all the correlated training samples belonging to the correct class are used to represent the test sample. The Lasso regularization was proposed...
In the area of multi-label image categorization, there are two important issues: label classification and label ranking. The former refers to whether a label is relevant or not, and the latter refers to what extent a label is relevant to an image. However, few existing papers have considered them in a holistic way. In this paper we will suggest a concrete improved method, named calibrated RankSVM,...
To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems...
Dynamic visual category learning calls for efficient adaptation as new training images become available or new categories are defined, existing training images or categories become modified or obsolete, or when categories are divided into subcategories or merged together. We develop novel methods for efficient incremental learning of SVM-based visual category classifiers to handle such dynamic tasks...
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction...
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