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Color composition is an important cue for image retrieval and object classification. In this paper we address the problem of inferring the color composition of visual objects from the pixel-level color distribution over the basic color terms. We build a discriminative model to tag each region with a dominant color and an associate one. We learn the human preference and cooccurrance patterns of the...
Labeling connected components and calculating the Euler number, connected-component number, and hole number in a binary image are usually necessary for image analysis, pattern recognition, and computer (robot) vision. This paper presents a new algorithm for calculating the Euler number, connected-component number, and hole number in a binary image by labeling connected components in the binary image...
Active learning traditionally assumes that the oracle is capable of providing labeling information for each query instance. In reality, the oracle might have no information for some queries and cannot provide accurate label but only answers “I don't know the label”. We focus on this problem and provide a unified objective function to ensure that each query instance submitted to the oracle is the one...
It is suggested how a Markov random field can be used for object tracking with context information. The tracking is formulated as a two layer process. In the first phase, the image is represented by a set of feature points which are tracked by a standard tracker. In the second phase, the proposed semi-supervised learning and labeling algorithm is used to label the points to three classes — object,...
A large amount of real-world data is required to train and benchmark any character recognition algorithm. Developing a page-level ground-truth database for this purpose is overwhelmingly laborious, as it involves a lot of manual efforts to produce a reasonable database that covers all possible words of a language. Moreover, generating such a database for historical (degraded) documents or for a cursive...
As more subject-specific image datasets (medical images, birds, etc) become available, high quality labels associated with these datasets are essential for building statistical models and method evaluation. Obtaining these annotations is a time-comsuming and thus a costly business. We propose a clustering method to support this annotation task, making the task easier and more efficient to perform...
With the explosive growth of Internet image data, labeling image data for image retrieval has become an increasingly onerous task. To that end, we proposed a novel multi-view learning with batch mode active learning framework, MV-BMAL, for improving the performance of image retrieval. Specifically, color, texture and shape features are extracted and considered as un-correlated and sufficient views...
Semantic image segmentation assigns a predefined class label to each pixel. This paper proposes a unified framework by using region bank to solve this task. Images are hierarchically segmented leading to region banks. Local features and high-level descriptors are extracted on each region of the bank. Discriminative classifiers are learned based on the histograms of feature descriptors computed from...
In a pattern recognition sequence consisting of alternating steps of interactive labeling, classifier training, and automated labeling (e.g., CAVIAR systems), the choice of sample size at each step affects the overall amount of human interaction necessary to label all the samples correctly. The appropriate splits depend on the error rate of the classifier as a function of the size of the training...
We investigate the application of structured output learning (SOL) in automatic annotation of court games. We formulate the problem of event classification in court games as one of learning a mapping from features to structured labels, and employ structured SVM to achieve a max-margin solution. We compare closely the more popular generative approach based on the hidden Markov model (HMM) with our...
We present an efficient method to compute similarity between graph nodes by comparing their neighborhood structures rather than proximity. The key is to use a hash for avoiding expensive subgraph comparison. Experiments show that the proposed algorithm performs well in semi-supervised node classification.
Support vector clustering (SVC) is a nonparametric clustering algorithm inspired by support vector machines. Incremental support vector clustering (ISVC) extends the SVC algorithm to an incremental version for the case of large-scale datasets with the assumption of no outliers. In order to tackle the problem of clustering large-scale noisy datasets, this paper proposes the algorithm termed incremental...
In this paper, we present a novel face alignment approach in thermal infrared face recognition. The alignment procedure is based on closest point set matching between sketch faces. Linear combination of positional and local pattern features is embedded in the pointwise distance to solve the local minimum problem for ICP due to edge noise in sketch faces. The comprehensive experiments, including intra-class,...
In this paper, a method named histogram intersection metric learning from scene tracks is proposed for automatic organizing people in videos. We make the following contributions: (i) learning histogram intersection distance instead of Mahalanobis distance for widely used face features; (ii) learning the metric from scene tracks without manually labeling any examples, which enables learning across...
We present a method to perform graph matching in which the human can interact and impose part of the graph labelling. Humans are very good at finding the correspondences between parts of two images but finding these correspondences is one of the most difficult tasks in pattern recognition. Through simple actions such as impose a node labelling or consider a node labelling is not correct; the user...
This paper focuses on producing fast and accurate co-segmentation to a pair of images that is scalable and able to apply multimodal features. We present a general solution for this purpose and specifically propose a noniterative and fully unsupervised method using pointwise color and regional covariance features for image co-segmentation. The scalability and generality of our method mainly attribute...
Discriminative approaches to human pose estimation have became popular in recent years. These approaches face a big challenge: Similar inputs might correspond to very dissimilar poses. This property misleads the mapping functions which rely on the Euclidean distances in the input space. In this paper, we use the distances between the labels of the training data to learn a metric and map the input...
Figure-ground labeling is a classical problem in computer vision in which the goal is to label different parts of the visual input as figural or background. Yet most existing approaches focuses on single image figure-ground labeling with little emphasis on video. We present a method which integrates several cues to achieve figure-ground labeling on video sequences. The method is evaluated on challenging...
Chemoinformatics aim to predict molecule's properties through informational methods. Computer science's research fields concerned with chemoinformatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework for combining these two fields. We present in this paper two contributions to this research field: a graph kernel based on an optimal linear combination...
This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is...
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