1,721,027 research outputs found
Cascade Learning by Optimally Partitioning
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Cascaded AdaBoost classifier is a well-known efficient object detection algorithm. The cascade structure has many parameters to be determined. Most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically. Their objective functions are not directly related to minimum computation cost. These algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost. On the assumption that a strong classifier is given, in this paper, we propose an optimal cascade learning algorithm (iCascade) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated. iCascade searches the optimal partition point r(i) of each stage by directly minimizing the computation cost of the cascade. Theorems are provided to guarantee the existence of the unique optimal solution. Theorems are also given for the proposed efficient algorithm of searching optimal parameters ri. Once a new stage is added, the parameter ri for each stage decreases gradually as iteration proceeds, which we call decreasing phenomenon. Moreover, with the goal of minimizing computation cost, we develop an effective algorithm for setting the optimal threshold of each stage. In addition, we prove in theory why more new weak classifiers in the current stage are required compared to that of the previous stage. Experimental results on face detection and pedestrian detection demonstrate the effectiveness and efficiency of the proposed algorithm.</p
Learning Sampling Distributions for Efficient Object Detection
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Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection. By sampling particle windows (PWs) from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and the number of PWs in each stage. Moreover, it has to generate too many PWs in the initialization step and it unnecessarily regenerates too many PWs around object-like regions. In this paper, we attempt to solve the problems of MPW. An important fact we used is that there is a large probability for a randomly generated PW not to contain the object because the object is a sparse event relative to the huge number of candidate windows. Therefore, we design a PD so as to efficiently reject the huge number of nonobject windows. Specifically, we propose the concepts of rejection, acceptance, and ambiguity windows and regions. Then, the concepts are used to form and update a dented uniform distribution and a dented Gaussian distribution. This contrasts to MPW which utilizes only on region of support. The PD of MPW is acceptance-oriented whereas the PD of our method (called iPW) is rejection-oriented. Experimental results on human and face detection demonstrate the efficiency and the effectiveness of the iPW algorithm. The source code is publicly accessible.</p
Classifying Discriminative Features for Blur Detection
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Blur detection in a single image is challenging especially when the blur is spatially-varying. Developing discriminative blur features is an open problem. In this paper, we propose a new kernel-specific feature vector consisting of the information of a blur kernel and the information of an image patch. Specifically, the kernel specific-feature is composed of the multiplication of the variance of filtered kernel and the variance of filtered patch gradients. The feature origins from a blur-classification theorem and its discrimination can also be intuitively explained. To make the kernel-specific features useful for real applications, we build a pool of kernels consisting of motion-blur kernels, defocus-blur (out-of-focus) kernels, and their combinations. By extracting such features followed by the classifiers, the proposed algorithm outperforms the state-of-the-art blur detection method. Experimental results on public databases demonstrate the effectiveness of the proposed method.</p
Efficient object detection by prediction in 3D space
No full textBecause the scale, horizontal and vertical coordinates of an object in an image are arbitrary, so object detection can be viewed as a process of searching the object in the 3D space spanned by the scale, horizontal, and vertical factors. Traditional sliding window based method has to exhaustively search and check the 3D space, resulting in prohibitive computation cost. To deal with this problem, in this paper, we propose to explore both the scaling capacity and translation capacity of object detector to accelerate detection speed, without loss of detection accuracy. In our paradigm, scaling capacity can relieve the use of all possible sizes of templates at the first stage, i.e., only a few number of templates that can cover a large range of target object size are used to coarsely find the targets. Similarly, translation capacity can avoid dense grid sampling at the very beginning. After initial estimation, further evaluations with templates of finer scales are carried out around the candidates to verify the existence of target objects. Moreover, different from traditional uniform grid scanning, we present an interlaced scanning method called diamond grid scanning which can reduce redundant evaluation. Experimental results on face detection demonstrate the advantage of our method. (C) 2014 Elsevier B.V. All rights reserved
Relevance and irrelevance graph based marginal Fisher analysis for image search reranking
No full textLearning-to-rank techniques have shown promising results in the domain of image ranking recently, where dimensionality reduction is a critical step to overcome the "curse of dimensionality". However, conventional dimensionality reduction approaches cannot guarantee the satisfying performance because the important ranking information is ignored. This paper presents a novel "Ranking Dimensionality Reduction" scheme specifically designed for learning-to-rank based image ranking, which aims at not only discovering the intrinsic structure of data but also keeping the ordinal information. Within this scheme, a new dimensionality reduction algorithm called Relevance Marginal Fisher Analysis (RMFA) is proposed. RMFA models the proposed pairwise constraints of relevance-link and irrelevance-link into the relevance graph and the irrelevance graph, and applies the graphs to build the objective function with the idea of Marginal Fisher Analysis (MFA). Further, a semi-supervised RMFA algorithm called Semi-RMFA is developed to offer a more general solution for the real-world application. Extensive experiments are carried on two popular, real-world image search reranking datasets. The promising results demonstrate the robustness and effectiveness of the proposed scheme and methods. (C) 2015 Elsevier B.V. All rights reserved
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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