The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we present four image descriptors for HEp-2 cell staining patterns classification, including LBP, Gabor, DCT, and a global appearance statistical descriptor. A multiclass boosting SVM algorithm is proposed to integrate these descriptors together: (1) within each boosting round, four multiclass posterior probability SVMs are trained corresponding to four descriptors, and then combined...
The challenge of single image dehazing mainly comes from the double uncertainty of scene depth and scene radiance. Approximation of the transmission, which encodes the scene depth information, is the most significant step to solve the dehazing problem. In this paper, we propose to derive an optimal transmission map under a heuristic assumption in the dehazing model. The proposed objective function...
Spectral regression discriminant analysis (SRDA) is an important subspace learning method. It has a tunable parameter, i.e., the regularization parameter, which critically affects the performance. However, how to set this parameter automatically has not been well solved to date. In SRDA, this regularization parameter was only set as a constant, which is usually suboptimal. In this paper, we develop...
Spatial pyramid matching (SPM) component is part of most state-of-art image classification methods. SPM encodes spatial distribution of image features, in an un-supervised fashion, by partitioning an image into regions at multiple scales and concatenating feature vectors for these regions. In this paper we propose to replace the unsupervised SPM procedure with a supervised two-stage feature selection...
In this paper, we propose an unsupervised cluster method via a multi-task learning strategy, called Mt-Cluster. Our MtCluster learns a cluster-specific dictionary for each cluster to represent its sample signals and a shared common pattern pool (the commonality) for the essentially complemental representation. By treating learning the cluster-specific dictionary as a single task, MtCluster works in...
Local Binary Descriptors (LBDs) are good at matching image parts, but how much information is actually carried? Surprisingly, this question is usually ignored and replaced by a comparison of matching performances. In this paper, we directly address it by trying to reconstruct plausible images from different LBDs such as BRIEF [4] and FREAK [1]. Using an inverse problem framework, we show that this...
We propose an integrated and personalized video retrieval and summarization system. We estimate and impose appropriate preference values on affinity propagation graph of the video frames. Then, our system produces the summary which is useful for the user in her/his relevance feedback and for the retrieval module for comparing video pairs. The experiments confirm the effectiveness of our approach for...
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...
Insect species recognition is more difficult than generic object recognition because of the similarity between different species. In this paper, we propose a hybrid approach called discriminative local soft coding (DLSoft) which combines local and discriminative coding strategies together. Our method takes use of neighbor codewords to get a local soft coding and class specific codebooks (sets of codewords)...
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 image classification, multi-scale information is usually combined by concatenating features or selecting scales. Their main drawbacks are that concatenation increases the feature dimensionality by the number of scales and scale selection typically loses the information from other scales. We propose to solve this problem by the dissimilarity representation as it enables to combine various sources...
We address the problem of featureless pattern recognition under the assumption that pair-wise comparison of objects is arbitrarily scored by real numbers. Such a linear embedding is much more general than the traditional kernel-based approach, which demands positive semi-definiteness of the matrix of object comparisons. This demand is frequently prohibitive and is further complicated if there exist...
In this paper, a novel spatial-temporal multi-scale method (STMSM) is proposed to solve the problem of detecting multiple moving objects on complex background. Moving objects have multi-scale features both in spatial and temporal domain. The motion salience sub-spaces determine the moving features including position, size and trajectory of each moving object, then the problem of detecting moving objects...
In this paper we present a method to recover a spectra representation for reproduction and recognition on multispectral imagery. To do this, we commence by viewing the spectra in the image as a mixture which can be expressed in terms of the sample mean and a set of basis vectors and weights. This treatment leads to an MAP approach where the sample means is given by the centers yielded by the application...
Object classification is of vital importance to intelligent traffic surveillance. A big challenge is that shooting view changes in different scenes, which leads to sharp accuracy decrease since training and test samples do not follow the same distribution anymore. On the other hand, manual labeling training samples is time and labor consuming. We propose a feature-based transfer learning framework...
We propose a novel trajectory clustering algorithm which is suitable for online processing of pedestrian or vehicle trajectories computed with a vision-based tracker. Our approach does not require defining distances between trajectories, and can thus process broken trajectories which are inevitable in most cases when object trackers are applied to real world video footage. Clusters are defined as...
Finding discriminant features is useful for pattern recognition applications. In this work, geometric matching is combined with linear discriminant analysis (LDA) to learn the importance of the features of symbols, and assign weights to these features accordingly. The features are the line segments of the symbols. We use geometric matching within a symbol spotting system to get information on the...
Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and spatial pyramid matching (SPM) achieved state-of-the-art performance in image classification. However, only adopting SPM to exploit spatial information is not enough for satisfactory performance. In this paper, we use hierarchical temporal memory (HTM) cortical learning algorithms to extend this LLC...
The study of neurological processes and pharmaceutical effects often relies on the analysis of mice behaviour. Automatic tracking tools are widely employed for this purpose, however they are mainly limited to a single mouse. We propose a real time segmentation and tracking algorithm able to manage multiple interacting mice regardless of their fur colour or light settings via an infrared camera. The...
Image retrieval is a well researched area and often based on integrating various kinds of image features. Apart from colour features, texture features are deemed crucial for successful image retrieval. Local binary pattern (LBP) based texture algorithms have gained significant popularity in recent years and have been shown to be useful for a variety of tasks. In this paper, we provide a comprehensive...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.