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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Hà Quang Minh Loris Bazzani Vittorio Murino Google MPI for Intelligent Systems Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging data sets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.

关键词： kernel methods manifold regularization multi-class classification multi-kernel learning multi-modality learning multi-view learning vector-valued RKHS

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Exploiting multiple detections to learn robust brightness transfer functions in re-identification systems

Exploiting multiple detections to learn robust brightness tr...

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IEEE International Conference on Image Processing

作者： Amran Bhuiyan Alessandro Perina Vittorio Murino Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy

ISBN: (纸本)9781479983407

Re-identification systems aim at recognizing the same individuals in multiple cameras and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of Cumulative Weighted Brightness Transfer Functions to model this appearance variations. It is multiple frame-based learning approach which leverages consecutive detections of each individual to transfer the appearance, rather than learning brightness transfer function from pairs of images. We tested our approach on standard multi-camera surveillance datasets showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.

关键词： Cameras Transfer functions Brightness Histograms Bismuth Robustness Image color analysis

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CNN based common approach to handwritten character recognition of multiple scripts

CNN based common approach to handwritten character recogniti...

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International Conference on Document Analysis and Recognition

作者： Durjoy Sen Maitra Ujjwal Bhattacharya Swapan K. Parui Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9781479918065

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official Indian script. Recognition accuracies are comparable with the state-of-the-art.

关键词： Handwriting recognition Databases Accuracy Training

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Learning with Dataset Bias in Latent Subcategory Models

Learning with Dataset Bias in Latent Subcategory Models

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IEEE Conference on computer vision and pattern Recognition

作者： Dimitris Stamos Andrew McDonald Samuele Martelli Vittorio Murino Moin Nabi Massimiliano Pontil Department of Computer Science University College London Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia

ISBN: (纸本)9781467369657

Latent subcategory models (LSMs) offer significant improvements over training linear support vector machines (SVMs). Training LSMs is a challenging task due to the potentially large number of local optima in the objective function and the increased model complexity which requires large training set sizes. Often, larger datasets are available as a collection of heterogeneous datasets. However, previous work has highlighted the possible danger of simply training a model from the combined datasets, due to the presence of bias. In this paper, we present a model which jointly learns an LSM for each dataset as well as a compound LSM. The method provides a means to borrow statistical strength from the datasets while reducing their inherent bias. In experiments we demonstrate that the compound LSM, when tested on PASCAL, LabelMe, Caltech101 and SUN09 in a leave-one-dataset-out fashion, achieves an average improvement of over 6.5% over a previous SVM-based undoing bias approach and an average improvement of over 8.5% over a standard LSM trained on the concatenation of the datasets.

关键词： Dataset Linux Security Module Bias Bias Bias Statistical Biases bias(electronics) Functional training Support Vector Network Objective function Learning train stock

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Violence detection in crowded scenes using substantial derivative

Violence detection in crowded scenes using substantial deriv...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Sadegh Mohammadi Hamed Kiani Alessandro Perina Vittorio Murino Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia Genova Italy

This paper presents a novel video descriptor based on substantial derivative, an important concept in fluid mechanics, that captures the rate of change of a fluid property as it travels through a velocity field. Unlike standard approaches that only use temporal motion information, our descriptor exploits the spatio-temporal characteristic of substantial derivative. In particular, the spatial and temporal motion patterns are captured by respectively the convective and local accelerations. After estimating the convective and local field from the optic flow, we followed the standard bag-of-word procedure for each motion pattern separately, and we concatenated the two resulting histograms to form the final descriptor. We extensively evaluated the effectiveness of the proposed method on five benchmarks, including three standard datasets (Violence in Movies, Violence In Crowd, and BEHAVE), and two new video-survelliance sequences downloaded from Youtube. Our experiments show how the proposed approach sets the new state-of-the-art on all benchmarks and how the structural information captured by convective acceleration is essential to detect violent episodes in crowded scenarios.

关键词： Force Acceleration Standards Motion pictures Video sequences Trajectory Dynamics

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A comparison of crowd commotion measures from generative models

A comparison of crowd commotion measures from generative mod...

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IEEE computer Society Conference on computer vision and pattern Recognition Workshops (CVPRW)

作者： Sadegh Mohammadi Hamed Kiani Alessandro Perina Vittorio Murino Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia Genova Italy

Detecting abnormal events in video sequences is a challenging task that has been broadly investigated over the last decade. The main challenges come from the lack of a clear definition of abnormality and from the scarcity, often absence, of abnormal training samples. To address these two shortages, the computer vision community made use of generative models to learn normal behavioral patterns in videos. Then, for each test observation, a (crowd) commotion measure is computed quantifying the deviation from the normal model. In this paper, we evaluated two different families of generative models, namely topic models, representing the standard choice, and the most recent Counting Grids which have never been considered for this task. Moreover, we also extended the 2D Counting Grid, introduced for the analysis of images, to three dimensions, making the model able to capture the spatial-temporal relationships of the videos. In the experimental section, we compared all the approaches on five challenging sequences showing the superiority of the 3-D counting grid.

关键词： Computational modeling Cameras Feature extraction Histograms Surveillance Context Video sequences

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Analyzing Tracklets for the Detection of Abnormal Crowd Behavior

Analyzing Tracklets for the Detection of Abnormal Crowd Beha...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Hossein Mousavi Sadegh Mohammadi Alessandro Perina Ryad Chellali Vittorio Murino Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia Genova Italy

This paper presents a novel video descriptor, referred to as Histogram of Oriented Tracklets, for recognizing abnormal situation in crowded scenes. Unlike standard approaches that use optical flow, which estimates motion vectors only from two successive frames, we built our descriptor over long-range motion trajectories which is called tracklets in the literature. Following the standard procedure, we divided video sequences in spatio-temporal cuboids within which we collected statistics on the tracklets passing through them. In particular, we quantized orientation and magnitude in a 2-dimensional histogram which encodes the motion patterns expected in each cuboid. We classify frames as normal and abnormal by using Latent Dirichlet Allocation and Support Vector Machines. We evaluated the effectiveness of the proposed descriptors on three datasets: UCSD, Violence in Crowds and UMN. The experiments demonstrated (i) very promising results in abnormality detection, (ii) setting new state-of-the-art on two of them, and (iii) outperforming former descriptors based on the optical flow, dense trajectories and the social force model.

关键词： Tracking Histograms Standards Training Trajectory Dynamics Computational modeling

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Latent subcategory models for pedestrian detection with partial occlusion handling

Latent subcategory models for pedestrian detection with part...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Samuele Martelli Marco San Biagio Vittorio Murino Pattern Analysis & Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy Pattern Analysis & Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Liguria IT Universita degli Studi di Verona Verona Italy

Pedestrian detection is one of the most important tasks in computer vision, especially in automotive and security applications. One of the most common problems in real scenarios is related to the detection of occluded pedestrians. In this paper, we propose a novel multi-cue pedestrian detection approach able to deal with non homogeneous object samples by learning latent subcategory models trained on both visual and depth-based features. We also propose a novel self-similarity based feature, namely SST D , to encode the homogeneity in appearance of pedestrians characterized by similar occlusion patterns. Experiments are performed on the Daimler Pedestrian Detection Benchmark Dataset showing the robustness of our approach in actual scenarios.

关键词： Support vector machines Training Histograms Feature extraction Standards Computational modeling Benchmark testing

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Fast single image dehazing through Edge-Guided Interpolated Filter

Fast single image dehazing through Edge-Guided Interpolated ...

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IAPR International Conference on Machine vision Applications (MVA)

作者： Ximei Zhu Ying Li Yu Qiao Shenzhen Key lab of Computer Vision Pattern Recognition The Chinese University of Hong Kong Hong Kong SAR

ISBN: (纸本)9781479982479

Images and videos taken in foggy weather often suffer from low visibility. Recent studies demonstrate the effectiveness of dark channel prior [3] and guided filter [4] based approaches for image dehazing. However, these methods require high computational cost which makes them infeasible for realtime and embedding systems. In this paper, we propose Edge-Guided Interpolated Filter (EGIF) for fast image and video dehazing. The main contributions are twofold. Firstly, we develop Guided Interpolated Filter (GIF) to significantly speed up the estimation of transmission map, which is the most computational cost step in previous methods. Secondly, we utilize edge map as guidance image in GIF to enhance the fine details in dehazed images. Experimental results show that GIF can largely improve the computational efficiency and achieve comparable dehazing performance as previous guided filter based methods. EGIF can further enhance the sharpness of transmission map. Our method can achieve real-time processing for image of size 1024 × 768 with single CPU core (2GHz).

关键词： Image edge detection Interpolation Videos Real-time systems Image restoration computer vision Atmosphere

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A direct approach for word and character segmentation in run-length compressed documents with an application to word spotting

A direct approach for word and character segmentation in run...

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International Conference on Document Analysis and Recognition

作者： Mohammed Javed P. Nagabhushan B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Segmentation of a text document into lines, words and characters is an important objective in application like OCR and related analytics. However in today's scenario, the documents are compressed for archival and transmission efficiency. Text segmentation in compressed documents warrants decompression, and needs additional computing resources. In this backdrop, the paper proposes a method for text segmentation directly in run-length compressed, printed English text documents. Line segmentation is done using the projection profile technique. Further segmentation into words and characters is accomplished by tracing the white runs along the base region of the text line. During the process, a run based region growing technique is applied in the spatial neighborhood of the white runs to trace the vertical space between the characters. After detecting the character spaces in the entire text line, the decision of word space and character space is made by computing the average character space. Subsequently based on the spatial position of the detected words and characters, their respective compressed segments are extracted. The proposed algorithm is tested with 1083 compressed text lines, and F-measure of 97.93% and 92.86% respectively for word and character segmentation are obtained. Finally an application of word spotting is also presented.

关键词： Optical character recognition software Image coding Adaptation models

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