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With the proliferation of online media services, video ads are pervasive across various platforms involving Internet services and interactive TV services. Existing research efforts such as Google AdSense and MSRA videosense/imagesense have been devoted to the less intrusive insertion of relevant textual or video ads in streams or Web pages through text/image/video content analysis whereas the inherent...
With the explosive growth of Web resources, how to mine semantically relevant images efficiently becomes a challenging and necessary task. In this paper, we propose a concept sensitive Markov stationary feature (C-MSF) to represent images and also present a classifier based scheme for web image mining. First, through analyzing the results of Google Image Searcher, we collect an image set, which are...
The performance of traditional image retrieval approaches remains unsatisfactory, as they are restricted by the wellknown semantic gap and the diversity of textual semantics. To tackle these problems, we propose an improved image retrieval framework when querying with an image. The framework considers not only the discriminative power of various visual properties but also the semantic representation...
This paper proposes a mesh simplification method based on facial features region partition for the special three-dimensional facial mesh model. According to distribution of feature points, the face is divided into several parts consisting of critical feature regional and non-critical feature regional. This method adjusts different areas using different curvature values. This algorithm also uses edge...
The training of the adaboost algorithm for face detection is time costly; it often needs days or weeks in the previous system. In this paper, we describe efficient optimization techniques and implement skills to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce...
A novel improved linear discriminant analysis (ILDA) method is presented. Comparing with LDA, under the condition of d < c -1, d and c are the dimensionality of feature subspace and the number of classes respectively, ILDA uniformly preserves the class distances of classpairs by rearranging the contribution of each class-pair to the generalized between-class scatter matrix after whitening within-class...
Particle swarm optimization was applied in BP neural network training. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in realities. Meanwhile, PSO-BP neural network is applied into classification of fabric defect. The method of orthogonal wavelet transform was used to decompose monolayer from fabric image. And the sub-images of...
The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras. As was known, the corresponding cameras matrix may then be determined, up to a projective ambiguity, from the computed F, so the following actions will be continued such as reconstruction and so on. In the paper, the method for computing F from a set of corresponding image points is described, and...
Text classification has been considered as a hot research area in data mining. This paper presents a new approach combining hidden Markov model (HMM) with support vector machine (SVM) for text classification. HMMs are used to as a feature extractor and then a new feature vector is normalized as the input of SVMs, so the trained SVMs can classify unknown texts successfully. The experimental results...
Most broadcast stations rely on TV logos to claim video content ownership or visually distinguish the broadcast from the interrupting commercial block. Detecting and tracking a TV logo is of interest to TV commercial skipping applications and logo-based broadcasting surveillance (abnormal signal is accompanied by logo absence). Pixel-wise difference computing within predetermined logo regions cannot...
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