the proceedings contain 506 papers. the topics discussed include: face shape recovery from a single image using CCA mapping between tensor spaces;classifiability-based optimal discriminatory projection pursuit;blindly...
ISBN:
(纸本)9781424422432
the proceedings contain 506 papers. the topics discussed include: face shape recovery from a single image using CCA mapping between tensor spaces;classifiability-based optimal discriminatory projection pursuit;blindly separating mixtures of multiple layers with spatial shifts;structure-perceptron learning of a hierarchical log-linear model;unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition;classification via semi-Riemannian spaces;mining compositional features for boosting;context-aware clustering;locally adaptive learning for translation-variant MRF image priors;semi-supervised SVM batch mode active learning for image retrieval;semi-supervised distance metric learning for collaborative image retrieval;multiple-instance ranking: learning to rank images for image retrieval;correlational spectral clustering;and a parallel decomposition solver for SVM: distributed dual ascend using fenchel duality.
this study presents a comprehensive analysis of methods, techniques, and technologies employed in facial emotion recognition in human-robot interaction (HRI). A total of 124 articles were reviewed, selected based on s...
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Colorization refers to the process of adding color to black & white images or videos. this paper extends the term to handle surfaces in three dimensions. this is important for applications in which the colors of a...
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ISBN:
(纸本)9780769549897
Colorization refers to the process of adding color to black & white images or videos. this paper extends the term to handle surfaces in three dimensions. this is important for applications in which the colors of an object need to be restored and no relevant image exists for texturing it. We focus on surfaces withpatterns and propose a novel algorithm for adding colors to these surfaces. the user needs only to scribble a few color strokes on one instance of each pattern, and the system proceeds to automatically colorize the whole surface. For this scheme to work, we address not only the problem of colorization, but also the problem of pattern detection on surfaces.
Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-cla...
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ISBN:
(纸本)9780769549897
Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computervision. It, however faces the major difficulty to incorporate robust functions, rather than the square data fit...
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ISBN:
(纸本)9780769549897
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computervision. It, however faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper we propose a new online framework enabling the use of l(1) sparse data fitting term in robust dictionary learning, notably enhancing the usability and practicality of this important technique. Extensive experiments have been carried out to validate our new framework.
In this paper, we tackle the problem of performing inference in graphical models whose energy is a polynomial function of continuous variables. Our energy minimization method follows a dual decomposition approach, whe...
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ISBN:
(纸本)9780769549897
In this paper, we tackle the problem of performing inference in graphical models whose energy is a polynomial function of continuous variables. Our energy minimization method follows a dual decomposition approach, where the global problem is split into subproblems defined over the graph cliques. the optimal solution to these subproblems is obtained by making use of a polynomial system solver. Our algorithm inherits the convergence guarantees of dual decomposition. To speed up optimization, we also introduce a variant of this algorithm based on the augmented Lagrangian method. Our experiments illustrate the diversity of computervision problems that can be expressed with polynomial energies, and demonstrate the benefits of our approach over existing continuous inference methods.
We address the problem of person identification in TV series. We propose a unified learning framework for multi-class classification which incorporates labeled and unlabeled data, and constraints between pairs of feat...
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ISBN:
(纸本)9780769549897
We address the problem of person identification in TV series. We propose a unified learning framework for multi-class classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. the method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning appr...
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ISBN:
(纸本)9780769549897
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computervision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computervision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.
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