OpenRaiser/Intern-Atlas huggingface dataset:
Bramble: A bayesian multiple-blob tracker
Authors: Michael Isard, John MacCormick
Venue: Conference ICCV 2001
Year: 2001

Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robust tracking of deforming foreground objects. It has been standard, however, to treat background and foreground modelling as separate processes-background subtraction is followed by blob detection and tracking-which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single-camera real-time surveillance applications. The first innovation is a multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects. This likelihood function has a rigorous mathematical basis: it is adapted from the theory of Bayesian correlation, but uses the assumption of a static camera to create a more specific background model while retaining a unified approach to background and foreground modelling. Second we introduce a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time. We show how a particle filter can be used to perform joint inference on both the number of objects present and their configurations. Finally we demonstrate that our system runs comfortably in real time on a modest workstation when the number of blobs in the scene is small.
A Novel Support Vector Classifier with Better Rejection Performance
Authors: Chao Yuan, David P. Casasent
Venue: Conference CVPR 2003
Year: 2003

Support vector machines (SVMs) have been successfully used in many classification fields. However, conventional SVMs do not consider rejecting inputs and thus suffer from false alarms. The first reason for this is that every input is assumed to belong to one of the object classes and is accepted in some class. In this paper, we show that the second reason is that conventional SVMs do not describe each object class well. Thus, use of an output threshold does not solve this problem. We present a new support vector representation and discrimination machine (SVRDM), which has a discrimination capability comparable to that of the conventional SVM, and also offers good rejection ability. False alarm rates are greatly reduced. We analyze the properties of these two classifiers (SVM and SVRDM) in transformed feature space and compare their performances using both synthetic and real data.
Face Recognition Based on Depth and Curvature Features
Authors: Gaile G. Gordon
Venue: Conference CVPR 1992
Year: 1992

Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher accuracy in describing surface-based events, are better suited to describe properties of the face in areas such as the cheeks, forehead, and chin, and are viewpoint invariant. Faces are represented in terms of a vector of feature descriptors. Comparisons between two faces is made based on their relationship in the feature space. The author provides a detailed analysis of the accuracy and discrimination of the particular features extracted, and the effectiveness of the recognition system for a test database of 24 faces. Recognition rates are in the range of 80% to 100%. In many cases, feature accuracy is limited more by surface resolution than by the extraction process.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Language-specific Models in Multilingual Topic Tracking
Authors: Leah S. Larkey, Fangfang Feng, Margaret E. Connell, Victor Lavrenko
Venue: Conference SIGIR 2004
Year: 2004

Topic tracking is complicated when the stories in the stream occur in multiple languages. Typically, researchers have trained only English topic models because the training stories have been provided in English. In tracking, non-English test stories are then machine translated into English to compare them with the topic models. We propose a native language hypothesis stating that comparisons would be more effective in the original language of the story. We first test and support the hypothesis for story link detection. For topic tracking the hypothesis implies that it should be preferable to build separate language-specific topic models for each language in the stream. We compare different methods of incrementally building such native language topic models.
Visual concepts for news story tracking: analyzing and exploiting the NIST TRECVID video annotation experiment
Authors: John R. Kender, Milind R. Naphade
Venue: Conference CVPR 2005
Year: 2005

In the summer of 2003, using an interactive intelligent tool, over 100 researchers in video understanding annotated from the NIST TRECVID database over 62 hours of news video spanning six months of 1998. These 47K shots with 43 3 K labels from over 1000 visual concept categories comprise the largest publicly available ground truth for this domain. Our analysis of this data, combining the tools of statistical natural language processing, machine learning, and computer vision, finds significant novel statistical patterns that can be exploited for the accurate tracking of the episodes of a given news story over time, by using semantic labels that are solely visual. We find that the ground "truth" is very muddy, but by using the feature selection tool of information gain, we extract 14 reliable visual concepts with mid-frequency use; all but one are visual concepts that refer to settings, rather than actors, objects, or events. We discover that the probability of another episode of a named story to recur after a gap of d days is proportional to 1/(d + 1). We define a novel similarity measure incorporating both semantic and temporal properties between episodes i and j as: Dice(i, j)/(1 + gap(i, j)). We exploit a low-level computer vision technique, normalized cut (Laplacian eigenmaps), for clustering these episodes into stories, and in the process document a weakness of this popular technique. We use these empirical results to make specific recommendations on how better visual semantic ontologies for news stories, and how better video annotation tools, should be designed.
Fast top-k retrieval for model based recommendation
Authors: Deepak Agarwal, Maxim Gurevich
Venue: Conference WSDM 2012
Year: 2012

A crucial task in many recommender problems like computational advertising, content optimization, and others is to retrieve a small set of items by scoring a large item inventory through some elaborate statistical/machine-learned model. This is challenging since the retrieval has to be fast (few milliseconds) to load the page quickly. Fast retrieval is well studied in the information retrieval (IR) literature, especially in the context of document retrieval for queries. When queries and documents have sparse representation and relevance is measured through cosine similarity (or some variant thereof), one could build highly efficient retrieval algorithms that scale gracefully to increasing item inventory. The key components exploited by such algorithms is sparse query-document representation and the special form of the relevance function. Many machine-learned models used in modern recommender problems do not satisfy these properties and since brute force evaluation is not an option with large item inventory, heuristics that filter out some items are often employed to reduce model computations at runtime.
Skin Detail Analysis for Face Recognition
Authors: Jean-Sebastien Pierrard, Thomas Vetter
Venue: Conference CVPR 2007
Year: 2007

This paper presents a novel framework to localize in a photograph prominent irregularities in facial skin, in particular nevi (moles, birthmarks). Their characteristic configuration over a face is used to encode the person's identity independent of pose and illumination. This approach extends conventional recognition methods, which usually disregard such small scale variations and thereby miss potentially highly discriminative features. Our system detects potential nevi with a very sensitive multi scale template matching procedure. The candidate points are filtered according to their discriminative potential, using two complementary methods. One is a novel skin segmentation scheme based on gray scale texture analysis that we developed to perform outlier detection in the face. Unlike most other skin detection/segmentation methods it does not require color input. The second is a local saliency measure to express a point's uniqueness and confidence taking the neighborhood's texture characteristics into account. We experimentally evaluate the suitability of the detected features for identification under different poses and illumination on a subset of the FERET face database.
A Reliable Skin Mole Localization Scheme
Authors: Taeg Sang Cho, William T. Freeman, Hensin Tsao
Venue: Conference ICCV 2007
Year: 2007

Mole pattern changes are important cues in detecting melanoma at an early stage. As a first step to automatically register mole pattern changes from skin images, this paper presents a framework to detect and label moles on skin images in the presence of clutter, occlusions, and varying imaging conditions. The input image is processed with cascaded blocks to successively discard non-mole pixels. Our method first searches the entire input image for skin regions using a non-parametric skin detection scheme, and the detected skin regions are further processed using a difference of Gaussian (DoG) filter to find possible mole candidates of varying sizes. Mole candidates are classified as moles in the final stage using a trained support vector machine. To increase the mole classification accuracy, hair is removed if present on the skin image using steerable filters and a graphical model. The performance of the designed system is evaluated with 28 test images, and the experimental results demonstrate the effectiveness of the proposed mole localization scheme.
Computer Vision for Music Identification
Authors: Yan Ke, Derek Hoiem, Rahul Sukthankar
Venue: Conference CVPR 2005
Year: 2005

We describe how certain tasks in the audio domain can be effectively addressed using computer vision approaches. This paper focuses on the problem of music identification, where the goal is to reliably identify a song given a few seconds of noisy audio. Our approach treats the spectrogram of each music clip as a 2D image and transforms music identification into a corrupted sub-image retrieval problem. By employing pairwise boosting on a large set of Viola-Jones features, our system learns compact, discriminative, local descriptors that are amenable to efficient indexing. During the query phase, we retrieve the set of song snippets that locally match the noisy sample and employ geometric verification in conjunction with an EM-based "occlusion" model to identify the song that is most consistent with the observed signal. We have implemented our algorithm in a practical system that can quickly and accurately recognize music from short audio samples in the presence of distortions such as poor recording quality and significant ambient noise. Our experiments demonstrate that this approach significantly outperforms the current state-of-the-art in content-based music identification.
Beyond the euclidean distance: Creating effective visual codebooks using the histogram intersection kernel
Authors: Jianxin Wu 0001, James M. Rehg
Venue: Conference ICCV 2009
Year: 2009

Common visual codebook generation methods used in a Bag of Visual words model, e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual code words. However, most popular visual descriptors are histograms of image measurements. It has been shown that the Histogram Intersection Kernel (HIK) is more effective than the Euclidean distance in supervised learning tasks with histogram features. In this paper, we demonstrate that HIK can also be used in an unsupervised manner to significantly improve the generation of visual codebooks. We propose a histogram kernel k-means algorithm which is easy to implement and runs almost as fast as k-means. The HIK codebook has consistently higher recognition accuracy over k-means codebooks by 2-4%. In addition, we propose a one-class SVM formulation to create more effective visual code words which can achieve even higher accuracy. The proposed method has established new state-of-the-art performance numbers for 3 popular benchmark datasets on object and scene recognition. In addition, we show that the standard k-median clustering method can be used for visual codebook generation and can act as a compromise between HIK and k-means approaches.
End-to-end scene text recognition
Authors: Kai Wang 0059, Boris Babenko, Serge J. Belongie
Venue: Conference ICCV 2011
Year: 2011

This paper focuses on the problem of word detection and recognition in natural images. The problem is significantly more challenging than reading text in scanned documents, and has only recently gained attention from the computer vision community. Sub-components of the problem, such as text detection and cropped image word recognition, have been studied in isolation [7, 4, 20]. However, what is unclear is how these recent approaches contribute to solving the end-to-end problem of word recognition. We fill this gap by constructing and evaluating two systems. The first, representing the de facto state-of-the-art, is a two stage pipeline consisting of text detection followed by a leading OCR engine. The second is a system rooted in generic object recognition, an extension of our previous work in [20]. We show that the latter approach achieves superior performance. While scene text recognition has generally been treated with highly domain-specific methods, our results demonstrate the suitability of applying generic computer vision methods. Adopting this approach opens the door for real world scene text recognition to benefit from the rapid advances that have been taking place in object recognition.
Indexing confusion networks for morph based spoken document retrieval
Authors: Ville T. Turunen, Mikko Kurimo
Venue: Conference SIGIR 2007
Year: 2007

In this paper, we investigate methods for improving the performance of morph-based spoken document retrieval in Finnish by extracting relevant index terms from confusion networks. Our approach uses morpheme-like subword units ("morphs") for recognition and indexing. This alleviates the problem of out-of-vocabulary words, especially with inflectional languages like Finnish. Confusion networks offer a convenient representation of alternative recognition candidates by aligning mutually exclusive terms and by giving the posterior probability of each term. The rank of the competing terms and their posterior probability is used to estimate term frequency for indexing. Comparing against 1-best recognizer transcripts, we show that retrieval effectiveness is significantly improved. Finally, the effect of pruning in recognition is analyzed, showing that when recognition speed is increased, the reduction in retrieval performance due to the increase in the 1-best error rate can be compensated by using confusion networks.
Clothing cosegmentation for recognizing people
Authors: Andrew C. Gallagher, Tsuhan Chen
Venue: Conference CVPR 2008
Year: 2008

Reseachers have verified that clothing provides information about the identity of the individual. To extract features from the clothing, the clothing region first must be localized or segmented in the image. At the same time, given multiple images of the same person wearing the same clothing, we expect to improve the effectiveness of clothing segmentation. Therefore, the identity recognition and clothing segmentation problems are inter-twined; a good solution for one aides in the solution for the other.
Adaptive message update for fast affinity propagation
Authors: Yasuhiro Fujiwara, Makoto Nakatsuji, Hiroaki Shiokawa, Yasutoshi Ida, Machiko Toyoda
Venue: Conference KDD 2015
Year: 2015

Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points. This is because it updates the messages of all data point pairs. This paper proposes an efficient algorithm that guarantees the same clustering results as the original algorithm. Our approach, F-AP, is based on two ideas: (1) it computes upper and lower estimates to limit the messages to be updated in each iteration, and (2) it dynamically detects converged messages to efficiently skip unneeded updates. Experiments show that F-AP is much faster than previous approaches with no loss in clustering performance.
An efficient algorithm for compressed mr imaging using total variation and wavelets
Authors: Shiqian Ma, Wotao Yin, Yin Zhang 0010, Amit Chakraborty
Venue: Conference CVPR 2008
Year: 2008

Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a very limited number of samples. Because information such as boundaries of organs is very sparse in most MR images, compressed sensing makes it possible to reconstruct the same MR image from a very limited set of measurements significantly reducing the MRI scan duration. In order to do that however, one has to solve the difficult problem of minimizing nonsmooth functions on large data sets. To handle this, we propose an efficient algorithm that jointly minimizes the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm, total variation, and a least squares measure, one of the most powerful models for compressive MR imaging. Our algorithm is based upon an iterative operator-splitting framework. The calculations are accelerated by continuation and takes advantage of fast wavelet and Fourier transforms enabling our code to process MR images from actual real life applications. We show that faithful MR images can be reconstructed from a subset that represents a mere 20 percent of the complete set of measurements.
Detection and removal of rain from videos
Authors: Kshitiz Garg, Shree K. Nayar
Venue: Conference CVPR 2004
Year: 2004

The visual effects of rain are complex. Rain consists of spatially distributed drops falling at high velocities. Each drop refracts and reflects the environment, producing sharp intensity changes in an image. A group of such falling drops creates a complex time varying signal in images and videos. In addition, due to the finite exposure time of the camera, intensities due to rain are motion blurred and hence depend on the background intensities. Thus, the visual manifestations of rain are a combination of both the dynamics of rain and the photometry of the environment. In this paper, we present the first comprehensive analysis of the visual effects of rain on an imaging system. We develop a correlation model that captures the dynamics of rain and a physics-based motion blur model that explains the photometry of rain. Based on these models, we develop efficient algorithms for detecting and removing rain from videos. The effectiveness of our algorithms is demonstrated using experiments on videos of complex scenes with moving objects and time-varying textures. The techniques described in this paper can be used in a wide range of applications including video surveillance, vision based navigation, video/movie editing and video indexing/retrieval.
Probabilistic connection importance inference and lossless compression of deep neural networks
Authors: Xin Xing, Long Sha, Pengyu Hong, Zuofeng Shang, Jun S. Liu
Venue: Conference ICLR 2020
Year: 2020

Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios. It is thus of interest to compress DNNs while maintaining their performance levels. We here propose a probabilistic importance inference approach for pruning DNNs. Specifically, we test the significance of the relevance of a connection in a DNN to the DNN’s outputs using a nonparemtric scoring testand keep only those significant ones. Experimental results show that the proposed approach achieves better lossless compression rates than existing techniques
Anomaly detection with robust deep autoencoders
Authors: Chong Zhou, Randy C. Paffenroth
Venue: Conference KDD 2017
Year: 2017

Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.