nils-herrmann/clean_scirepeval huggingface dataset:
Generalized Semi-supervised and Structured Subspace Learning for Cross-Modal Retrieval

Motivated by the fact that unlabeled data can be easily collected and help to exploit the correlations among different modalities, this paper proposes a novel method named generalized semi-supervised structured subspace learning (GSS-SL) for the task of cross-modal retrieval. First, to predict more relevant class labels for unlabeled data, we propose a label graph constraint that ensures the intrinsic geometric structures of different feature spaces consistent with that of label space. Second, considering that class labels directly reveal the semantic information of multimedia data, GSS-SL takes the label space as a linkage to model the correlations among different modalities. Concretely, the label graph constraint, label-linked loss function, and regularization are integrated into a joint minimization formulation to learn a discriminative common subspace. Finally, an efficient optimization algorithm is designed to alternately optimize multiple linear transformations for different modalities and update the class indicator matrices for unlabeled data. Furthermore, an arbitrary number of modalities can be solved in the proposed framework. Extensive experiments on three standard benchmark datasets demonstrate that GSS-SL outperforms previous methods on exploiting the correlations among different modalities.
Integrating Relevance Feedback in Boosting for Content-Based Image Retrieval

Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In this paper, we propose a novel interactive boosting framework to integrate user feedback into boosting scheme and bridge the gap between high-level semantic concept and low-level image features. Our method achieves more performance improvement from the relevance feedback than AdaBoost does because human judgment is accumulated iteratively to facilitate learning process. It also has obvious advantage over the classic relevance feedback method in that the classifiers are trained to pay more attention to wrongfully predicted samples in user feedback through a reinforcement training process. An interactive boosting scheme called i.Boost is implemented and tested using adaptive discriminant projection (ADP) as base classifiers, which not only combines but also enhances a set of ADP classifiers into a more powerful one. To evaluate its performance, several applications are designed on UCI benchmark data sets, Harvard, UMIST, ATT facial image data sets and COREL color image data sets. The proposed method is compared to normal AdaBoost, classic relevance feedback and the state-of-the-art projection-based classifiers. The experiment results show the superior performance of i.Boost and the interactive boosting framework.
An object based watermarking solution for MPEG4 video authentication

This paper presents an object based watermarking solution for MPEG4 video authentication. The watermark is embedded in the discrete Fourier transform (DFT) domain before MPEG4 encoding. Groups of DFT coefficients in the low-middle frequency band are selected for watermarking. The coefficients in every group are divided into two sub-groups based on a pre-defined pattern, and the energy relationship between these two sub-groups is used to hide the watermark. The experimental results show that our algorithm is robust against MPEG4 compression as well as object based video manipulations such as rotation, scaling and inaccurate segmentation.
Semantic Analysis and Personalization for Mobile Media Applications

The explosive proliferation of multimedia content leads to an increasing need for its ubiquitous access, anytime and anywhere, and over a wide variety of receiving devices. Mobile media is a high growth area due to rich multimedia content and rapidly increased number of portable multimedia players. However, current consumer expectations for mobile media technologies and applications still far exceed the achievement of today's state of the art technologies. This paper gives an overview of critical technologies for mobile media and emphasizes on state-of-the-art in semantic analysis and personalization for mobile media applications as well as addresses research challenges for mobile media technologies and applications.
Weak signal detection method of adaptive digital lock-in amplifier based on particle swarm optimization algorithm

In this paper, an intelligent algorithm — Particle Swarm Optimization (PSO) is applied to adaptively realize weak sinusoidal signal detection of digital lock-in amplifier. Each particle is a reference signal. Using this algorithm PSO first searches the optimum particle of particle swarm, and then estimates the corresponding parameters of input useful signal through the messages of the optimum particle. The algorithm can realize detection of weak signal by modifying its some parameters, so it is more convenient and flexible than the method of traditional lock-in amplifier (LIA) in application. Computer simulation results show that this method can accurately detect multiple useful sinusoidal signals which are inundated by strong noise of −40 dB, and also detect AM signal. Then the effectiveness of this method is verified by AM signal.
Spatial evolutionary spectrum and beamspace MUSIC for high-speed DOA estimation

We combine the concepts of evolutionary spectrum and array processing and set a high-speed method to estimate the direction-of-arrival (DOA) of nonstationary signals using the theories of cross-power evolutionary periodogram (CEP) and beamforming. The proposed method implements three-step DOA estimation. First, we estimate the cross-power evolutionary of the sensor array using the minimized mean-squared error. Second, we estimate the DOA roughly using the beamspace MUSIC method. Lastly, we accurately estimate the DOA with the result of the coarse estimation.
Photometric Stereo for General BRDFs via Reflection Sparsity Modeling

This paper proposes a pixelwise photometric stereo method for object surfaces with general bidirectional reflectance distribution functions (BRDFs) via appropriate reflection modeling. The modeling is based on three general characteristics of reflection components, i.e., the smooth variation of diffuse reflection, the concentration of specular reflection, and the low-intensity nature of shadow. A graph, whose nodes are light directions, is introduced to model these characteristics. In the graph, the neighborhood of nodes is determined by finding the light sources with close directions. The smoothness of the diffuse component is termed as the summation of local variations under all light sources. The specular reflection is modeled by group sparsity, and the shadow is determined via weighted ℓ1-norm modeling. The optimization problem, which incorporates these three modeling terms, is cast as a second-order cone programming problem. The proposed method is evaluated on both synthetic and real-world scenes with both isotropic and anisotropic materials. The experimental results show that the method is effective for object surfaces with general BRDFs and outperforms the state-of-the-arts.
Adaptive Discriminant Projection for Content-based Image Retrieval

Content-based image retrieval (CBIR) is a computer vision application that aims at automatically retrieving images based on their visual content. Linear discriminant analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps the original high-dimensional space to a low-dimensional one and preserves the most discriminant features. Those techniques assume images from certain class(es) are all visually similar and try to cluster them in the projected space. In this paper we show that the human high-level concept of semantic similarity between images may not arise only from the low-level visual similarity and consequently that assumption is inappropriate in many cases. We propose an adaptive discriminant projection (ADP) framework which could model different data distributions based on the clustering of different classes. To learn the best model fitting the real scenario, boosted adaptive discriminant projection is further proposed. Extensive experiments are designed to evaluate our methods and compare them to the state-of-the-art techniques on benchmark data set and real image retrieval applications. The results show the superior performance of our proposed methods
Highway traffic information extraction from Skycam MPEG video

Skycams are video cameras that are mounted at high altitudes to provide images of large areas. We present an algorithm that quickly extracts highway traffic information, such as average vehicle speed and density, directly from MPEG compressed Skycam video. Our experiments show that 80-90% of estimation accuracy can be achieved for both vehicle speed and density. The algorithm is stable and effective for Skycam, which the traditional approaches cannot handle due to the low resolution. Because a Skycam can cover a much longer stretch of road than a close-view camera that is used in the current traffic monitoring system, great savings can be expected by widely deploying a Skycam based traffic monitoring system.
RIDE: Reversal Invariant Descriptor Enhancement

In many fine-grained object recognition datasets, image orientation (left/right) might vary from sample to sample. Since handcrafted descriptors such as SIFT are not reversal invariant, the stability of image representation based on them is consequently limited. A popular solution is to augment the datasets by adding a left-right reversed copy for each original image. This strategy improves recognition accuracy to some extent, but also brings the price of almost doubled time and memory consumptions. In this paper, we present RIDE (Reversal Invariant Descriptor Enhancement) for fine-grained object recognition. RIDE is a generalized algorithm which cancels out the impact of image reversal by estimating the orientation of local descriptors, and guarantees to produce the identical representation for an image and its left-right reversed copy. Experimental results reveal the consistent accuracy gain of RIDE with various types of descriptors. We also provide insightful discussions on the working mechanism of RIDE and its generalization to other applications.
A New Maximum Power Point Tracking Algorithm for Solar Vehicle

To save energy, solar vehicle have come into being. Therefore, the study of photovoltaic maximum power point tracking is necessary. Traditional photovoltaic maximum power point tracking (MPPT) algorithm such as constant voltage algorithm can not track the maximum power point quickly and accurately. Operation voltage can not be adjusted rapidly with environmental changes when using conductance adding algorithm and disturbance observation algorithm. Both of these algorithms are prone to mistaken judgments and unstable output voltage. These shortcomings have resulted in inaccurate sampling. This article presents a new maximum power point tracking algorithm for solar vehicle. The algorithm use engineering model and a numerical method to track the maximum power point with variable step. The system performance was evaluated by modeling and simulation in Matlab/simulink. Simulation results demonstrate that the new algorithm is superior to current methods for finding the maximum power point, in terms of both speed and accuracy.
A Spectral Method for Period Detection of PN Sequence for Weak DS-SS Signals in Dynamic Environments

In this paper, we develop a method based on the reprocessing of the power spectrum density (PSD) of direct sequence spread spectrum (DS-SS) signals with residual carrier, it can solve difficult problem of period detection and estimation of the pseudo noise (PN) sequence for weak DS-SS signals in dynamic environments. The method is developed from another method, which is based on the reprocessing of DS-SS signals’ PSD in normal environments before. The first step of the method is to calculate the PSD of weak DS-SS signals in dynamic environments; then, we use the calculated PSD as an input signal to calculate their PSD again; the second PSD result will appear some regular tip-pulses at the integer-times of PN sequence’s period. We can detect and estimate the period of PN sequence from the interval measurement between the regular tip-pulses of the second PSD result in the end. The computer simulations show that the method can work well on lower signal to noise ratio (SNR) input signals with larger residual carrier in dynamic environments.
Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification

We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) framework for person re-identification. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information to improve re-identification accuracy. Both low level features and semantic/data-driven attributes are utilized. Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people. The learning objective function consists of a quadratic loss regarding class labels and an attribute embedding error, which is solved by an alternating optimization procedure. Experiments on three person re-identification datasets have demonstrated that MTL-LORAE outperforms existing approaches by a large margin and produces state-of-the-art results.
Semi-fragile watermarking algorithm of color image based on Slant Transform and channel coding

In the paper, a new method for semi-fragile watermarking of color image based on Slant Transform and channel coding is proposed. This technique certifies the belongings of images by using authentication watermarks that are embedded in IF of Slant Transform regions. Recover manipulated regions by restore watermarks that are extracted from the former 11 coefficients of each block. Restore watermarks are embedded in the least significant bits of each pixels. For increasing robustness of the algorithm, calculate error correction coding and spread spectrum modulation to authentication watermarks before embedding them. Watermarking extraction does not need original image, it belongs to blind extraction.
The estimation of the PN sequence's period of the DSSS signals in narrowband interference environment

In this paper, a combination method is presented to estimate the PN sequence's period of the direct sequence spread spectrum (DSSS) signal in strong narrow-band interference environment. An overlapping windowed FFT algorithm is first used to transform the received data signals to the frequency-domain where the elimination or drastical attenuation of the strong narrow-band interference is performed. Then a reprocessing of the power spectrum density algorithm is proposed to detect and estimate the Pseudo-random code (PN) sequence's period of the DSSS signal in low SNR, which solves the difficult problem of PN sequence's periodic detection and estimation of weak DSSS signals with interference. Theoretical analysis and simulation results show that the proposed algorithm can effectively estimate the PN sequence's period of DSSS signal, and the estimating performance closely relates the low SNR.
Direction-of-arrival estimation of wideband chirp signals using the Wigner distribution of symmetrical sensor array

Recently, time-frequency techniques have been successfully applied in narrowband direction-of-arrival (DOA) estimation (see Amin, M.G., 1999; Belouchrani, A. and Amin, M.G., 1999; Zhang, Y. et al., 2001; 2000). A novel time-frequency approach to direction finding of wideband chirp signals is proposed. Our technique employs the Wigner-Ville distribution (WVD) of a symmetrical sensor array to get direction vectors of wideband chirp signals. By time-frequency selectivity, a new data model is constructed to estimate the DOA of each signal one by one. A subspace method is exploited to deduce the DOA from the new model. The computation cost of our method is low because no focussing or interpolation is needed. Simulation results illustrate the performance of our method.
Automatic labeling and classification of brain CT images

Automatic medical image classification is difficult because of the lacking of training data. As manual labeling is too costly, we provide an automatic labeling solution to this problem by making use of the radiology report associated with the medical images. We first segment and reconstruct the 3D regions of interest (ROIs) from the medical images, and extract pathology and anatomy information from the associated report. We use an anatomical atlas to map the ROIs to the anatomy part(s) and match the pathology information of the same anatomy part(s) from the text. In this way, the ROIs are automatically labeled with pathology types which can be served as class labels, and a training data set of a large number of training instances is generated automatically. We extract the volume, color, location, and shape features of the ROIs, and classify the types of ROIs using these features. The overall evaluation result is promising to doctors and medical professionals. Our experiment is conducted using traumatic brain injury CT images; however, our framework of automatically labeling and classifying medical cases can be extended to medical images in other modality or of other anatomical part.
Efficient Relevance Feedback Using Semi-supervised Kernel-specified K-means Clustering

In this paper, we present an efficient and convenient relevance feedback (RF) by using a semi-supervised kernel-specified k-means clustering (SKKC) technique. SKKC is used to cluster the retrieval results so that RF can be conducted on the cluster level. Compared with traditional RF conducted on the point/single-image level, the new RF will facilitate the RF selection and reduce user's efforts on it. Furthermore, the proposed approach enables an accumulated learning ability by recording and learning from the history of users' RFs. The new RF is applied in a content-based medical image retrieval (CBMIR) system. Experimental results on ImageCLEF database of around 9,000 images have shown that the proposed new RF is able to improve effectiveness and efficiency of CBMIR
Joint Estimation of Cyclic Frequency and Direction of Arrival Based on Cyclic-Frequency-Beamspace

A new method for estimation of cyclic frequency and direction of arrival (DOA) is presented. A cyclic-frequency-beamspace data model is established, and then is used to estimate cyclic frequency and DOA by using Pro-ESPRIT, beamspace-ESPRIT and an integer search algorithm. Numerical simulation results illustrate the availability of the proposed method
Common business process model for continuous SDP transformation

In the telecom industry, it becomes increasingly important for operators to provide a service delivery platform (SDP) quickly to improve time-to-revenue cycle of new innovative value-added services at lower cost. But the actual spectrum of services delivered by SDP is not yet clearly defined and differs from vendor to vendor. It will lead to the silo SDP problem. The SDP transformation solution intends to offer a horizontal SDP framework (metaSDP) which has the capability to work in concert with existing SDP silos. The common business process model (CBPM), which based on eTOM, is used to defined the general processed used in the metaSDP. To leverage the legacy carrierpsilas service platforms, the SDP specified processes are added into the eTOM with all level3 and level4 business elements, and modeled in IBM Websphere business modeler (WBM). The SDP business process instantiation method are also introduced here to describe how to create the service delivery processes quickly by this model and templates.
Minimum Norm Least Squares Extrapolation Estimate for Discrete (a,b,c,d)-Bandlimited Signals

This letter investigates the extrapolation problem of discrete (a,b,c,d)-bandlimited signals. Based on operator notations, we first show that the extrapolation problem amounts to finding an admissible solution of an under-determined system. Then the minimum norm least squares (MNLS) solution is shown to be an admissible extrapolation estimate. Besides, singular value decomposition method is used to set up an extrapolation formula which guarantees that the extrapolation result achieves the MNLS estimate. An iterative extrapolation algorithm is also proposed to achieve the MNLS estimate. Finally, numerical results are given to demonstrate the efficiency of the proposed extrapolation algorithms.
Fine-Grained Image Search

Large-scale image search has been attracting lots of attention from both academic and commercial fields. The conventional bag-of-visual-words (BoVW) model with inverted index is verified efficient at retrieving near-duplicate images, but it is less capable of discovering fine-grained concepts in the query and returning semantically matched search results. In this paper, we suggest that instance search should return not only near-duplicate images, but also fine-grained results, which is usually the actual intention of a user. We propose a new and interesting problem named fine-grained image search, which means that we prefer those images containing the same fine-grained concept with the query. We formulate the problem by constructing a hierarchical database and defining an evaluation method. We thereafter introduce a baseline system using fine-grained classification scores to represent and co-index images so that the semantic attributes are better incorporated in the online querying stage. Large-scale experiments reveal that promising search results are achieved with reasonable time and memory consumption. We hope this paper will be the foundation for future work on image search. We also expect more follow-up efforts along this research topic and look forward to commercial fine-grained image search engines.
Update relevant image weights for content-based image retrieval using support vector machines

Relevance feedback (Y. Rui et al., 1998) has been a powerful tool for interactive content based image retrieval (CBIR). During the retrieval process, the user selects the most relevant images and provides a weight of preference for each relevant image. The user's high level query and perception subjectivity can be captured to some extent by dynamically updated low-level feature weights based on the user's feedback. However, in MARS (Y. Rui et al., 1997), only the positive feedbacks, i.e., relevant images are considered. A novel approach is proposed by providing both positive and negative feedbacks for support vector machine (SVM) learning. The SVM learning results are used to update the weights of preference for relevant images. Priorities are given to the positive feedbacks that have larger distances to the hyperplane determined by the support vectors. This approach releases the user from manually providing preference weight for each positive example, i.e., relevant image as before. Experimental results show that the proposed approach has reasonable improvement over relevance feedback with possible examples only.