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Logo spotting is of a great interest because it enables to categorize the document images of a digital library of scanned documents according to their sources, without any costly semantic analysis of their textual transcript. In this paper, we present an approach for logo spotting, based on the matching of keypoints extracted both from the query document images and a given set of logos (gallery) using...
In this paper we propose two new types of features useful for problems in which one wants to describe object or image relationships rather than objects or images themselves. The features are based on the notion of distribution flow, as derived from the classic Transportation Problem. Two variants of such features, the Distribution Flow (DFlow) and Displacement Field (DField), are defined and studied...
We use images that have been collected using an Internet search engine to train color name models for color naming and recognition tasks. Considering color histogram bands as being words of an image and the color names as classes, we use the supervised latent Dirichlet allocation to train our model. To pre-process the training data, we use state-ofthe art salient object detection and a Kullback-Leibler...
This paper presents a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features. Since a single low-level feature based representation is not enough to capture the variations of human appearance, multiple low-level features (i.e., optical flow and histogram of gradient 3D features) are fused to further improve recognition performance...
Indirect immunofluorescence (IIF) imaging is a method used for detection of antinuclear auto-antibodies (ANA) for the diagnosis of autoimmune diseases. We present a feature extraction and classification scheme to classify the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary features that are sensitive to staining pattern variations among classes. Our feature...
Semantic interpretation and understanding of images is an important goal of visual recognition research and offers a large variety of possible applications. One step towards this goal is semantic segmentation, which aims for automatic labeling of image regions and pixels with category names. Since usual images contain several millions of pixel, the use of kernel-based methods for the task of semantic...
Although texture features are important for region-based classification of remote sensing images, the literature shows that texture descriptors usually have poor performance when compared and combined with color descriptors. In this paper, we propose a bag-of-visual-words (BOW) “propagation” approach to extract texture features from a hierarchy of regions. This strategy improves efficacy of feature...
Scene detection is the fundamental step for efficient accessing and browsing videos. In this paper, we propose to segment movie into scenes which utilizes fused visual and audio features. The movie is first segmented into shots by an accelerating algorithm, and the key frames are extracted later. While feature movies are often filmed in open and dynamic environments using moving cameras and have continuously...
In this work1, we propose a novel approach for image categorization, which we will refer to as Bag-of-Scenes (BoS). It is based on the association of Sparse coding (Sc) and pooling techniques applied to histograms of multi-scale Local Binary Patterns (LBP) and its improved variant. This approach can be considered as a 2-layer hierarchical architecture. The first layer, encodes general local patch's...
Deformable registration of multi-modality medical image remains a challenging research topic. The incorporation of prior information on the expected joint distribution has shown to noticeably improve registration accuracy and robustness. However, direct application of the learned joint histogram makes the algorithm sensitive to the difference between the training data and the test image. This paper...
In the field of computer vision, pyramid matching by minimization has gained increasing popularity. This paper points out and discusses an inherent anomaly in pyramid matching by minimization that can affect the performance of classification approaches based on this type of matching. As a solution, a new multiresolution measure, called Manhattan-Pyramid Distance (MPD), is proposed. Systematic evaluations...
In this paper, we develop a feature-aware 4D spatiotemporal image registration method. Our model is based on a 4D (3D+time) free-form B-spline deformation model which has both spatial and temporal smoothness. We first introduce an automatic 3D feature extraction and matching method based on an improved 3D SIFT descriptor, which is scale- and rotation- invariant. Then we use the results of feature...
The paper introduces a novel approach to place representation for robot localization and mapping. It uses classical invariance theory while proposing an adaptive kernel to omnidirectional images and exploiting only the main significant visual information in the images. The approach is validated in real world robot exploration and localization and compared to color histograms.
Feature plays an important role in pedestrian detection, and considerable progress has been made on shape-based descriptors. However, color cues have barely been devoted to detection tasks, seemingly due to the variable appearance of pedestrians. In this paper, Color Maximal-Dissimilarity Pattern (CMDP) is proposed to encode color cues by two core operations, i.e., oriented filtering and max-pooling,...
In order to achieve high accuracy of face recognition, detection of facial parts such as eyes, nose, and mouth is essentially important. In this paper, we propose a method to detect eyes from frontal face images. The proposed method consists of two major steps. The first is two dimensional Hough transformation for detecting circle of unknown radius. The circular Hough transform first generates two...
Early Recognition of human activities is a highly desirable functionality for many visual intelligent systems. However, in computer vision, very few work have been devoted to this challenging and interesting task. In this paper, we address human activity early recognition as a pattern recognition problem of time series data. A new model called ARMA-HMM is introduced to integrate both the predictive...
Facial emotion recognition-the detection of emotion states from video of facial expressions-has applications in video games, medicine, and affective computing. While there have been many advances, an approach has yet to be revealed that performs well on the non-trivial Audio/Visual Emotion Challenge 2011 data set. A majority of approaches still employ single frame classification, or temporally aggregate...
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...
Center-surround measurements are widely used for saliency detection but with some disadvantages: 1) Center-surround operation may cause inaccurate segmentation and even involve incorrect detection results; 2) In most situations, only using center-surround feature is not efficient to encode object saliency. To overcome these disadvantages, we describe a novel measurement, namely Corner-Surround Contrast...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
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