We propose novel photography recomposition method, which aims at transferring the photography composition of a reference image to an input image automatically. Without any user interaction, our approach first identies the salient foreground objects or image regions of interest, and the recomposition is performed by solving a graph-matching based optimization task. With additional post-processing step to preserve the locality and boundary information of the recomposed visual components, we can solve the task of photography recomposition without the uses of any prior knowledge on photography or predetermined image aesthetics rules. Experiments on a variety of images, including transferring the photography composition from real photos, sketches or even paintings, would conrm the eectiveness of our proposed method.
We propose a novel discriminative clustering algorithm with a hierarchical framework for solving unsupervised image segmentation problems. Our discriminative clustering process can be viewed as an EM algorithm, which alternates between the learning of image visual appearance models and the updates of cluster labels (i.e., segmentation outputs) for each image segment. In particular, we advance a simple-to-complex strategy during the above process, which allows the learning of a series of classifiers with different generalization capabilities from the input image, so that consecutive image segments can be well separated. With the proposed hierarchical framework, improved image segmentation can be achieved even if the shapes of the segments are complex, or the boundaries between them are ambiguous. Our work is different from existing region or contour-based approaches, which typically focus on either separating local image regions or determining the associated contours. Our experiments verify that we outperform state-of-the-art approaches on unsupervised image segmentation.
Abstract—In this paper, we present a new basis of polynomial over finite fields of characteristic two and then apply it to the encoding/decoding of Reed-Solomon erasure codes. The proposed polynomial basis allows that h-point polynomial evaluation can be computed in O(h log2(h)) finite field operations with small leading constant. As compared with the canonical polynomial basis, the proposed basis improves the arithmetic complexity of addition, multiplication, and the determination of polynomial degree from O(h log2(h) log2 log2(h)) to O(h log2(h)). Based on this basis, we then develop the encoding and erasure decoding algorithms for the (n = 2r; k) Reed-Solomon codes. Thanks to the efficiency of transform based on the polynomial basis, the encoding can be completed in O(n log2(k)) finite field operations, and the erasure decoding in O(n log2(n)) finite field operations. To the best of our knowledge, this is the first approach supporting Reed-Solomon erasure codes over characteristic-2 finite fields while achieving a complexity of O(n log2(n)), in both additive and multiplicative complexities. As the complexity leading factor is small, the algorithms are advantageous in practical applications.
This paper considers a noncoherent distributed space-frequency coded (SFC) wireless relay system with multiple relays. Each relay adopts a censoring scheme to determine whether the relay will decode and forward the source's information towards the destination. We analytically obtain the achievable diversity for both cases of perfect and imperfect relay censoring. With perfect censoring, we show that the same diversity of a conventional noncoherent SFC MIMO-OFDM system is achievable in the considered noncoherent distributed SFC system with maximum likelihood (ML) decoding, regardless of whether partial information of channel statistics and relay decoding status is available at the destination. With imperfect censoring, we analytically investigate how censoring errors affect the achievability of the system's diversity. We show that the two types of censoring errors, which correspond to useless and harmful relays, respectively, can decrease the achievable diversity significantly. Our analytical insights and numerical simulations demonstrate that the noncoherent distributed system can offer a comparable diversity as the conventional MIMO-OFDM system if relay censoring is carefully implemented.
N/A
In recent years, advances in virtualization technology have enabled multiple virtual machines to run on a physical machine, such that each virtual machine can perform independently with its own operating system. The IT industry has adopted virtualization technology because of its ability to improve hardware resource utilization, achieve low-power consumption, support concurrent applications, simplify device management, and reduce maintenance costs. However, because of the hardware limitation of storage devices, the I/O capacity could cause performance bottlenecks. To address the problem, we propose a hybrid storage access framework that exploits solid-state drives (SSDs) to improve the I/O performance in a virtualization environment.
Location-based services allow users to perform check-in actions, which not only record their geo-spatial activities, but also provide a plentiful source for data scientists to analyze and plan a more accurate and useful geographical recommender system. In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data. The central idea is that the pleasure of staying at the locations along a route is significantly affected by their visiting time. Each location has its own proper visiting time due to the category, objective, and population. To consider the visiting time of locations into route planning, we develop a three-stage time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations constructed, we devise an inference method, LocTimeInf , to predict and recover the location visiting time on routes. Second, we aim to find the representative and popular time-aware location-transition behaviors from user check-in data, and a Time-aware Transit Pattern Mining (TTPM) algorithm is proposed. Third, based on the mined time-aware transit patterns, we develop a Proper Route Search (PR-Search) algorithm to construct the final time-aware routes for recommendation. Experiments on Gowalla check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.
N/A
In this paper, we study the sum degrees of freedom (DoF) of an uplink two-cell multiuser MIMO interference network with asymmetric number of users in the cells. The achievable DoF is devised based on a two-dimensional space-time spreading code framework with linear precoding/decoding design and finite channel extension. The derivation of the achievable DoF is shown related to a rank minimization problem, which corresponds to the minimization of the dimension of the interference subspace. The problem is solved by the proposed grouping algorithm (GA) based on aligning interfering signals into a low-dimensional subspace as a group and attaining the minimum number of groups. The achievable sum DoF derived based on the proposed GA is shown to be greater than prior arts and achieves the theoretic upper bound in several cases. We also give a closed-form expression of the maximum achievable sum DoF when there is the maximum number of admissible users in the considered finite diversity environment.
There has been an increasing attention on learning feature representations from the complex, high-dimensional audio data applied in various music information retrieval (MIR) problems. Unsupervised feature learning techniques, such as sparse coding and deep belief networks have been utilized to represent music information as a term-document structure comprising of elementary audio codewords. Despite the widespread use of such bag-of-frames (BoF) model, few attempts have been made to systematically compare different component settings. Moreover, whether techniques developed in the text retrieval community are applicable to audio codewords is poorly understood. To further our understanding of the BoF model, we present in this paper a comprehensive evaluation that compares a large number of BoF variants on three different MIR tasks, by considering different ways of low-level feature representation, codebook construction, codeword assignment, segment-level and song-level feature pooling, tf-idf term weighting, power normalization, and dimension reduction. Our evaluations lead to the following findings: 1) modeling music information by two levels of abstraction improves the result for difficult tasks such as predominant instrument recognition, 2) tf-idf weighting and power normalization improve system performance in general, 3) topic modeling methods such as latent Dirichlet allocation does not work for audio codewords.