nils-herrmann/clean_scirepeval huggingface dataset:
NBgossip - Neighborhood Gossip with Network Coding Based Message Aggregation

Gossip based algorithms for information dissemination have recently received significant attention for sensor and ad hoc network applications due to their simplicity and robustness. However, a common drawback of many gossip based protocols is the waste of energy in passing redundant information over the network. Thus gossip algorithms need to be re-engineered in order to be applicable for energy constrained networks. In this paper, we consider a scenario where each node in the network holds a piece of information (message) at the beginning, and the objective is to simultaneously disseminate all information (messages) among all nodes quickly and cheaply. To provide a practical solution to this problem for ad hoc and sensor networks, NBgossip algorithm is proposed, which is based on network coding and neighborhood gossip. In NBgossip, nodes do not simply forward messages they received, instead, the linear combinations of the messages are sent out. In addition, every node exchanges messages with its neighboring nodes only. Mathematical proof and simulation studies show that the proposed NBgossip terminates in the optimal O(n)-order rounds and outperforms the existing gossip based approaches in terms of energy incurred to spread all information.
Convolutional networks for voting-based anomaly classification in metal surface inspection

Automated Visual Inspection (AVI) systems for metal surface inspection is increasingly used in industries to aid human visual inspectors for classification of possible anomalies. For classification, the challenge lies in having a small and specific dataset that may easily result in over-fitting. As a solution, we propose to use deep Convolutional Neural Networks (ConvNets) learnt from the large ImageNet dataset [9] for image representations via transfer learning. Since a small dataset cannot be used to fine-tune a ConvNet due to overfitting, we also propose a Majority Voting Mechanism (MVM), which fuses the features extracted from the last three layers of ConvNets using Support Vector Machine (SVM) classifiers. This classification framework is effective where no prior knowledge of the best performing ConvNet layers is needed. This also allows flexibility in the choice of ConvNet used for feature extraction. The proposed method not only outperforms state-of-the-art traditional hand-crafted features in terms of classification but also obtains good results compared to other deep ConvNet features extracted from a pre-selected best layer on several anomaly and texture datasets.
Image Retargeting in Compressed Domain

A simple algorithm for image retargeting in the compressed domain is proposed. Most existing retargeting algorithms work directly in the spatial domain of the raw image. Here, we work on the DCT coefficients of a JPEG-compressed image to generate a gradient map that serves as an importance map to help identify those parts in the image that need to be retained during the retargeting process. Each 8×8 block of DCT coefficients is scaled based on the least importance value. Retargeting can be done both in the horizontal and vertical directions with the same framework. We also illustrate image enlargement using the same method. Experimental results show that the proposed algorithm produces less distortion in the retargeted image compared to some other algorithms reported recently.
Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map

A framework is presented for estimating the pose of a camera based on images extracted from a single omnidirectional image of an urban scene, given a 2D map with building outlines with no 3D geometric information nor appearance data. The framework attempts to identify vertical corner edges of buildings in the query image, which we term VCLH, as well as the neighboring plane normals, through vanishing point analysis. A bottom-up process further groups VCLH into elemental planes and subsequently into 3D structural fragments modulo a similarity transformation. A geometric hashing lookup allows us to rapidly establish multiple candidate correspondences between the structural fragments and the 2D map building contours. A voting-based camera pose estimation method is then employed to recover the correspondences admitting a camera pose solution with high consensus. In a dataset that is even challenging for humans, the system returned a top-30 ranking for correct matches out of 3600 camera pose hypotheses (0.83% selectivity) for 50.9% of queries.
An efficient video classification system based on HMM in compressed domain

In this paper, we present our method for effective and efficient classification of different types of video that make use of the low level visual features, color and motion features. The color features, similar to the MPEG-7 scalable color descriptors, are the compressed color histograms extracted from the DC images of the video sequences. The motion features, similar to MPEG-7 motion activity descriptors, are extracted from the motion vectors. Both features are extracted from the compressed domain and give a good characterization of the video in both the spatial and temporal dimension. We then classify different types of video using hidden Markov model. First, we train the hidden Markov model for each type of video. Then the trained HMMs are used to classify incoming videos using maximum likelihood classification.
Coherent Phrase Model for Efficient Image Near-Duplicate Retrieval

This paper presents an efficient and effective solution for retrieving image near-duplicate (IND) from image database. We introduce the coherent phrase model which incorporates the coherency of local regions to reduce the quantization error of the bag-of-words (BoW) model. In this model, local regions are characterized by visual phrase of multiple descriptors instead of visual word of single descriptor. We propose two types of visual phrase to encode the coherency in feature and spatial domain, respectively. The proposed model reduces the number of false matches by using this coherency and generates sparse representations of images. Compared to other method, the local coherencies among multiple descriptors of every region improve the performance and preserve the efficiency for IND retrieval. The proposed method is evaluated on several benchmark datasets for IND retrieval. Compared to the state-of-the-art methods, our proposed model has been shown to significantly improve the accuracy of IND retrieval while maintaining the efficiency of the standard bag-of-words model. The proposed method can be integrated with other extensions of BoW.
Hierarchicalword image representation for parts-based object recognition

Many multimedia applications can benefit from recognizing image content. It requires a robust and discriminative representation of objects, especially in the situation of only a few training samples available. In this paper, we present a new approach to integrate the advantages of bag-of-words model and part-based model for image recognition. Each image is encoded as a Hierarchical Word Image (HWI), which contains not only visual appearance but also spatial information. The object parts are then located and represented in HWI. Finally, the part-based Star Model (SM) is used to learn the object model and recognize the test images. It is shown that our proposed approach can detect more accurate part candidates and significantly improve the performance of original part-based model for object recognition.
A repository adapter for resource management information

Integrated network management frameworks for self-managing systems in a grid environment consisting of disparate applications, devices and subsystems require the use of a common definition of these managed resources. The common information model (CIM) provides such a standard for their description. However, the CIM specification lacks formalism which limits its use in knowledge aggregation and reasoning. This paper discusses the design of a repository adapter for resource information modeled in CIM. The adapter translates CIM constructs to an ontology-based language, the Data Centre Markup Language (DCML), thereby formalizing the model. Issues encountered during this process are identified and areas for future work are discussed.
Semantics in service discovery and QoS measurement

For the vision of ubiquitous, easily invokable Web services to become a reality, it will be necessary for service requesters to compare the quality-of-service (QoS) aspects of various services. So standardized QoS information must become a part of the service discovery process. This article surveys the current research on QoS and service discovery, including ontologies such as OWL-S and DAML-S. The authors then propose their own ontology, DAML-QoS, which provides detailed QoS information in a DAML format.
Using material classification methods for steel surface defect inspection

Steel surface defect inspection is an important industrial application of machine vision that can be used to inspect the quality of many products. Most researchers in this field usually start from a task and design task-specific methods for such task. However, most surface defects can be viewed as surface areas that have abnormal material characteristics and material classification is an extensively studied field in computer vision. It will be very interesting to explore how the state-of-the-art method designed for general material classification works for domain-specific applications like defect inspection and how to improve it so that it can be adapted to real industrial applications where training samples are usually difficult to collect. In this paper, we will show our finding that the state-of-the-art general method for material classification can achieve better performance than the current best tailored method for steel surface defect inspection. In order to handle the difficulties in collecting training samples, we propose a new method called Local SIFT Pattern (LSP), based on the general method. The LSP can achieve satisfactory accuracy with extremely limited training samples.
A unified approach to detection of shot boundaries and subshots in compressed video

This paper describes a method to partition a video sequence into shots and subshots. By subshots, we mean one or a combination of the three camera motions of pan, tilt and zoom. The proposed technique detects both hard cuts and gradual transitions in MPEG compressed video using a single technique. We also present a motion estimation algorithm to compute the dominant motion represented by an affine model. The motion information is used to refine the location of dissolves as well as to subdivide the shot into subshots, thus providing a characterization of camera motion. We consider the dissimilarity between the I-, P- and B-frames with respect to the type of macroblocks used for encoding. Unlike previous algorithms reported, our method requires minimal decompression of the video sequence and uses very loose thresholds. The algorithm is evaluated on several types of video sequences to demonstrate its effectiveness.
Image model based on salient regions and its applications

Detection, representation, and training are the three main issues that need to be resolved in an object recognition or classification system. One possible method is using collection of regions to represent object categories where each region has a distinctive feature. In this paper we present a region-based image model which learn and classify objects by training the image model with variant of the objects within the same category. Each object category is represented by a constellation of representative parts. These regions are detected by salient region detector over suitable scales. The standard disjunction rule is applied to construct the image model. During the learning procedure the distance between any two regions is calculated and accumulated as a measure which is inversely proportional to the probability of a match. The regions with large distances are removed from the image model iteratively. Finally, a small set of regions is kept as the image model. This image model can be used to retrieve similar images or for object classification. Experimental results show the method is easy to calculate and efficient
A ZGPCA Algorithm for Subspace Estimation

We propose a new algorithm called the ZGPCA algorithm for subspace estimation based on the GPCA (generalized principal component analysis) algorithm. It is formulated within an FIR filter framework so that the norm vectors of the sub-spaces correspond to filter coefficients. It is shown that such an approach leads to a more accurate and computationally efficient method compared to the GPCA algorithm. We extend the ZGPCA algorithm to make it recursive so that subspaces with possibly different dimensions can be obtained. We also propose a new distance measure that can be used for k-means clustering of sample points within a subspace. Experimental results on synthetic data and applications on face clustering and sports video clustering show good performance of the proposed algorithm.
An improved distortion model for rate control of DCT-based video coding

This paper presents a rate control algorithm for the dominant discrete cosine transform (DCT)-based video coding. It is developed based on a more accurate rate-distortion (R-D) model, specifically, a new distortion-quantization (D-Q) model. Different from previous work that employs a uniform D-Q model or an empirical distortion model, our work proposes an accurate distortion model, which can quantitatively describe the relationship of distortion with respect to video source information and the selected quantization resolution. Based on understanding the distribution of source frequency coefficients and the quantization theory, our distortion model is proposed. This distortion model is combined with the classical R-D theory to generate a new rate model. Finally, the proposed model is implemented on an MPEG-4 encoder to perform rate control for a low-delay visual communication system. We also compare the proposed rate control with the VM18 rate control and it has been shown to be more efficient
MPEG-21 digital item adaptation by applying perceived motion energy to H.264 video

The new MPEG-21 standard defines a multimedia framework to enable transparent and augmented use of multimedia resources across heterogeneous networks and devices used by different communities. In this paper, we incorporated the perceived motion energy (PME) model into the proposed MPEG-21 digital item adaptation framework for frame dropping in H.264 encoded video adaptation. There are two advantages of this work, one is the use of PME model to reduce the viewer's perceived motion jitter due to frame dropping to a minimum. The other is the adaptation nodes can easily apply frame dropping operations without knowledge of detailed encoding syntax of H.264 videos.
Click4BuildingID@NTU: Click for Building Identification with GPS-enabled Camera Cell Phone

A working prototype of a building identification service which can be used on any camera cell phones equipped with GPS capability has been developed. Users can simply snap photos of architectures and send them, together with the corresponding GPS coordinates, via MMS to a remote server. The server will match the photos with the stored, GPS-tagged images using a combination of scale saliency algorithm for feature matching and earth movers distance measure for scene matching. The estimated location and other information are then sent back to the users via MMS. This prototype will have better accuracy than systems which rely solely on photo recognition given the exploitation of GPS information. Moreover, it is computationally lighter since the recognition engine only needs to compare stored images which lie within the GPS coordinates error range. It is relatively inexpensive as no special phones or subscriptions to telecommunication providers for provision of GPS equivalent location data (i.e.cell location) are needed.
Adaptive local context suppression of multiple cues for salient visual attention detection

Visual attention is obtained through determination of contrasts of low level features or attention cues like intensity, color etc. We propose a new texture attention cue that is shown to be more effective for images where the salient object regions and background have similar visual characteristics. Current visual attention models do not consider local contextual information to highlight attention regions. We also propose a feature combination strategy by suppressing saliency based on context information that is effective in determining the true attention region. We compare our approach with other visual attention models using a novel average discrimination ratio measure.
Concept model-based unsupervised web image re-ranking

Current large scale image retrieval engines rely heavily on the surrounding text information, which inevitably includes some irrelevant images in the retrieval results due to the noisy environment. To improve the retrieval performance, we propose an unsupervised web image re-ranking method by incorporating images' visual information. Our method can automatically select a set of representative images from the original image pool as concept model, which is highly related to the query concept and critically important for the re-ranking result. With a similarity graph constructed by top results given by text based retrieval, we utilize Normalized Cut to select the part with the highest similarity density as concept model. We re-rank the rest images according to their similarities to the concept model. The advantages of our method are (i): Our method is unsupervised, and it doesn't need any pre-prepared query/training image or user's feedback, Thus it greatly facilitates users' retrieval. (ii): By finding a set of images rather than single image, we are able to give a more complete and more robust model for the query concept. (iii): Multi-ranking Integration Strategy is adopted to re-rank the rest images. Experiments show that our method can achieve satisfying results.
Stratification-Based Keyframe Cliques for Effective and Efficient Video Representation

As there is an exponential increase of web videos, it is time-consuming to get a query result from the tremendous data. An effective and efficient video management system is in urgent need. To increase the efficiency of video retrieval and storage, the most widely used methods are indexing schemes, such as locality sensitive hashing (LSH). However, it is more essential to represent the video itself compactly. In this paper, we propose a strategy to generate stratification-based keyframe cliques (SKCs) for video description, which are more compact and informative than frames or keyframes. The new representations are scalable for different retrieval tasks due to the ranking of SKCs. To further accelerate the retrieval speed, only top SKCs will be used; meanwhile, the searching results are still satisfactory. Experiments are conducted on TRECVID dataset as well as web video dataset. Results show that our proposed SKCs are more succinct and informative for video retrieval and management.
A fast sparse reconstruction approach for high resolution image-based object surface anomaly detection

We propose an approach to resolve two issues in a recent proposed sparse reconstruction based, anomaly detection approach as a part of automated visual inspection (AVI). The original approach needs large computation and memory for high resolution problem. To solve it, we proposed a two-step sparse reconstruction, 1) the first sparse representation of input image is estimated in a sparse reconstruction with low resolution downsampled images and 2) the high resolution residual values is generated in another sparse reconstruction with the sparse representation. The first step provides the flexibility of freely adjusting the computation and the demand of memory storage with small trade-off of detection accuracy. Moreover, an illumination adaptive threshold with morphological operators is used in the anomaly classification. Empirical results show that the proposed approach can effectively replace the original approach with better results.
Concurrent Single-Label Image Classification and Annotation via Efficient Multi-Layer Group Sparse Coding

We present a multi-layer group sparse coding framework for concurrent single-label image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes image content as a whole, and tags, which describe the components of the image content. Therefore we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. To make our model more suitable for nonlinear separable features, we also extend our multi-layer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS), which further improves performances of image classification and annotation. Moreover, we also integrate our multi-layer group sparse coding with kNN strategy, which greatly improves the computational efficiency. Experimental results on the LabelMe, UIUC-Sports and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks.
Codebook+: A New Module for Creating Discriminative Codebooks

In this paper, we introduce a new module, Codebook+, into a classical framework which combines bag-of-words image representation with probabilistic latent semantic analysis (pLSA) for unsupervised object categorization. This new module makes the framework less sensitive to the image sampling methods as well as improves its performance. In this module, we create a new codebook based on the discriminability of each codeword in the original codebook for different categories. In our experiments, we compare the classification results of the framework with and without Codebook+ using five different image sampling methods.
Learning instance-to-class distance for human action recognition

In this paper, we propose a large margin framework to learn the local instance-to-class distance function using local patch-based feature vectors, which satisfies the property that distance from instance to its own class should be less than the distance to other class. This instance-to-class distance is modeled as the weighted combination of the distance from every patch in test image to its nearest patch in training class, where the weight is learned through the above learning phase. We evaluate the proposed method on human action datasets and compare with related methods. It is shown that the proposed method achieves promising performance and improves the efficiency.
Characterization and source modeling of MPEG-2 VBR video source

This paper presents the results of our study on the statistical characterization of MPEG-2 VBR video stream and the modeling of such source. The frame trace, marginal distribution of the frame size and the high autocorrelation existing in the continuous frames are analyzed. An ARMA process is used to model MPEG-2 video source. The different frame types of MPEG-2 video are considered together by performing a normalization transformation during modeling. The performance of the model is examined and the results show that the model is good in fitting the histogram and preserving the property of autocorrelation. We also indicate that the proper order of the ARMA model depends on the frame pattern of the MPEG source.