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This paper presents a new discriminative learning framework to associate the relationship between the objects and the words in an image and perform template matching scheme for complex association patterns. The problem is first formulated as a bipartite graph matching problem. Thereafter, structural support vector machine (SVM) is employed to obtain the optimal compatibility function to encode the...
The widespread use of handwritten signatures for idnetity authentication has resulted in a need for automated verification systems that make use of modern electronic devices (e.g., scanners, cameras, and digitizing tablets). However, there is still significant room for improvement in the performance of these automated systems when compared with that of human analysis, particularly that of forensic...
A decision-level fusion (DLF)-based team tactics estimation method in soccer videos is newly presented. In our method, tactics estimation based on audio-visual and formation features is newly adopted since the tactics of the soccer game are closely related to the audio-visual sequences and player positions. Therefore, by using these features, we classify the tactics via Support Vector Machine (SVM)...
The advantage of using collaborative brain-computer interfaces in improving human response in visual target recognition tests was investigated. We used a public EEG dataset created from recordings made using a 32-channel EEG system by Delorme et al. (2004) to compare the classification accuracy using one, two, and three EEG signal sets from different subjects. Fourteen participants performed a go/no-go...
In this paper, we propose a tourism category classification method based on estimation of reliable decision. The proposed method performs tourism category classification using location, visual, and textual tag features obtained from tourism images in image sharing services. As the biggest contribution of this paper, the proposed method performs successful classification based on two classification...
Bag-of-Features (BOF) representation is a very popular model for content based image classification. In BOF, term frequency (tf) and inverse document frequency (idf) is a very popular model to compute the weights of the visual vocabularies. However, tf-idf model does not contain the class information of images. Fortunately, chi-square model contains the class information well. So, in order to enhance...
We present a plant image recognition system geared towards plants with flowers. The system uses local invariants with Dense SIFT features and Bag of Visual Words representation, while the classification is done using Support Vector Machines. Our approach contains a pre-classification stage where images are categorized into color subgroups, to reduce the complexity of the problem. Using a 161-class...
The goal of this study is to explore the advantages of representing natural images with the cortical Layer-4 processing, which is the first step in visual information processing performed by the cerebral cortex of the brain. A cortical module, a macrocolumn, receives input from a small visual field and its Layer 4 performs a nonlinear transform of this input to generate its pluripotent representation...
In this paper we propose a new hyperplane fitting classification method that does not have limitations of the existing hyperplane fitting classifiers. There are two principal improvements of the proposed method: It returns sparse solutions and it is suitable for large-scale problems. Both advantages are accomplished by using a simple trick, which constraints positive samples to lie between two parallel...
Web-scale image understanding is a challenging but significant task to comprehend image contents on the internet. The de-facto standard methods based on machine learning or computer vision still suffer from a phenomenon of visual pol-ysemia and concept polymorphism (VPCP). To resolve the VPCP, Vicept has been proposed to characterize the membership distribution between visual appearances and semantic...
This paper proposes an adaptive bag-of-phrases (BoP) algorithm for mobile scene recognition based on bag-of-words approach. Conventional BoW methods do not consider the dependence and pairwise relationship among different codewords. However, these contextual relations between pairwise codewords play an important role for users to recognize an image. In light of this problem, this paper proposes an...
Action scenes usually contain higher motion activity than other scenes in feature films while showing events like fights, gun shots and car crashes. This work investigates motion and event detection to separate action scenes from non-action scenes. In contrast to existing work, the proposed system does not consider the shot structure of video. The approach uses SVMs to classify GIST-based global motion...
The bag-of-visual-words (BoW) representation has received wide application and public acceptance for visual categorization. However, the histogram based image representation ignores the spatial information and correlations among visual words. To tackle these problems, in this paper, we propose to use some image regions called ‘components’, as the higher-level visual elements to represent an image...
Based on MILES algorithm, we propose a novel multiple instance learning approach which regards visual word dictionary as feature space, and combines segmentation for object detection and extraction in the process of instance classification. This approach uses "Bag of Words" model. The whole image is considered as a multiple instance bag. The visual words that represent the image are regarded...
This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eye-tracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic...
Visual object categorisation (VOC) has become one of the most actively investigated topic in computer vision. In the mainstream studies, the topic is considered as a supervised problem, but recently, the ultimate challenge has been posed: Unsupervised visual object categorisation. Hitherto only a few methods have been published, all of them being computationally demanding successors of their supervised...
Visual concept detection is one of the most important tasks in image and video indexing. This paper describes our system in the ImageCLEF@ICPR Visual Concept Detection Task which ranked first for large-scale visual concept detection tasks in terms of Equal Error Rate (EER) and Area under Curve (AUC) and ranked third in terms of hierarchical measure. The presented approach involves state-of-the-art...
Representing image using the distribution of local features on a group of visual words is an effective method for visual categorization. Visual words can be related conceptually and the information can be incorporated to enhance the performance. However, conventional methods usually use visual words independently without considering this. This paper proposes a novel approach to measure the conceptual...
In this paper, we propose Semi-Supervised pLSA(SSpLSA) for image classification. Compared with the classic non-supervised pLSA, our method overcomes the shortcoming of poor classification performance when the features of two categories are quite similar. By introducing category label information into EM algorithm, the iteration process can be directed carefully to the desired result. SS-pLSA greatly...
In this paper, we propose a new method to select relevant images to the given keywords from the images gathered from the Web. Our novel method is based on the probabilistic latent semantic analysis (PLSA) model, which is a generative probabilistic topic model. Firstly, we gather images related to the given keywords from the Web with Web search engines. Secondly, we choose pseudo-training images from...
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