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The manual process for privacy setting could be very time-consuming and challenging for common users. By assuming that there are hidden correlations between the visual properties of images (i.e., visual features) or object classes and the privacy settings for image sharing, an effective algorithm is developed in this paper to achieve automatic prediction of image privacy, so that the best-matching...
In this paper, we propose a visual saliency detection algorithm to explore the fusion of various saliency models in a manner of bootstrap learning. First, an original bootstrapping model, which combines both weak and strong saliency models, is constructed. In this model, image priors are exploited to generate an original weak saliency model, which provides training samples for a strong model. Then,...
Artificial awareness is an interesting way of realizing artificial intelligent perception for machines. Since the foreground object can provide more useful information for perception and informative description of the environment than background regions, the informative saliency characteristics of the foreground object can be treated as a important cue of the objectness property. Thus, a sparse reconstruction...
This paper surveys the learning algorithms of visual features representation and the computational modelling approaches proposed with the aim of developing better artificial object recognition systems. It turns out that most of the learning theories and schemas have been developed either in the spirit of understanding biological facts of vision or designing machines that provide better or competitive...
Traditional bag-of-features approaches often vector-quantise the features into a visual codebook. This process inevitably causes loss of information. Recently codebook-free methods that avoid the vector-quantisation step have become more popular. Used in conjunction with nearest-neighbour approaches these methods have shown remarkable classification performance. In this paper we show how to exploit...
In this paper, we present a direct application of Support Vector Machine with Augmented Features (AFSVM) for video concept detection. For each visual concept, we learn an adapted classifier by leveraging the pre-learnt SVM classifiers of other concepts. The solution of AFSVM is to re-train the SVM classifier using augmented feature, which concatenates the original feature vector with the decision...
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...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
We present a novel bottom-up saliency detection algorithm. Our method computes so-called local regression kernels (i.e., local features) from the given image, which measure the likeness of a pixel to its surroundings. Visual saliency is then computed using the said ldquoself-resemblancerdquo measure. The framework results in a saliency map where each pixel indicates the statistical likelihood of saliency...
Visual target classification is one of the most important issues addressed in wireless multimedia sensor network (WMSN). This paper proposes a hybrid Gaussian process based classification method to implement binary visual classification (human/nonhuman) in WMSN. Because the computation ability of sensor node in WMSN is strictly limited, target classification is achieved by Gaussian process classifier...
This paper presents how to extract non-linear features by linear PCA. KPCA is effective but the computational cost is the drawback. To realize both non-linearity and low computational cost simultaneously, the idea of local kernel is used. The mapped features of the polynomial kernel can be described explicitly. When input features are divided into some local features and the polynomial kernel is applied...
We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. The problem is cast in a statistical hypothesis testing framework. The image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor. Due to clutter and occlusions, this set is expected to...
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