nils-herrmann/clean_scirepeval huggingface dataset:
Visual stem mapping and Geometric Tense coding for Augmented Visual Vocabulary

This paper addresses the problem of affine distortions caused by viewpoint changes for the application of image retrieval. We study how to expand the visual words from a query image for better retrieval recall without the sacrifice of retrieval precision and efficiency. Our main contribution is the building of visual dictionaries that retain the mapping relationships between visual words extracted from different viewpoints of the same object. Additionally, in each mapping rule we record the affine transformation in which the two visual words are related, as a compact code of viewpoints relationships. By analogizing the concepts of verb stem and verb tense in text, we use Visual Stems to denote visual words extracted from robust local patches, and record the relationships between their affine variants as visual stem mapping rules, including the geometric relationships coded as Geometric Tenses. In this way, our method augments original visual vocabulary with sufficient and accurate expansion information. In query phase, only the objects corresponding to the same visual stems and coherent geometric tense codes will be regarded as similar ones. Moreover, the mapping rules can be learned offline with only one sample for each object. Experiments show that our method can support efficient object retrieval with high recall, requiring little extra time and space cost over traditional visual vocabularies.
Automatic foreground segmentation using light field images

Foreground segmentation is a fundamental method in computer vision. Traditional foreground segmentation algorithms are sensitive to blurry degree of background, smooth foreground regions and camouflage foreground. To deal with these problems, we use light field images as input by exploiting its focusness cue. In this paper, we propose an automatic foreground segmentation algorithm for light field images. Firstly we calculate focusness measures in refocused stack and oversegment all focus images. Then the graph cut framework is introduced to generate foreground result. Experiments show that our method has a superior performance against counterparts and is more robust in scenes like ambiguous foreground.
Linearized comparison theorems for a nonlinear delay 2D discrete dynamic systems

This paper deals with a class of a nonlinear delay 2D discrete dynamical systems of the form A/sub m+1,n/+A/sub m,n+1/-A/sub mn/+/spl Sigma//sub i=1//sup /spl kappa//Pi(m,n)f/sub i/(A/sub m+/spl sigma/i,n/+A/sub m,n+/spl sigma/i/)=0, (E). We show that if a positive solution exists, then a related minorant equation has a positive solution as well. Similar results for a dual equation are also obtained. In fact, we remark further that equation (E) may also be regarded as a discrete analog of partial differential equations of the form /spl part/u//spl part/x+/spl part/u//spl part/y-u(x,y)+/spl Sigma//sub i=1//sup /spl kappa//Pi(x,y)f/sub i/(u(x+/spl sigma//sub i/,y)+u(x,y+/spl sigma//sub i/))=0. Therefore, qualitative of (E) may yield useful information for this companion partial differential equation.
Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles

Image content analysis is an important surround perception modality of intelligent vehicles. In order to efficiently recognize the on-road environment based on image content analysis from the large-scale scene database, relevant images retrieval becomes one of the fundamental problems. To improve the efficiency of calculating similarities between images, hashing techniques have received increasing attentions. For most existing hash methods, the suboptimal binary codes are generated, as the hand-crafted feature representation is not optimally compatible with the binary codes. In this paper, a one-stage supervised deep hashing framework (SDHP) is proposed to learn high-quality binary codes. A deep convolutional neural network is implemented, and we enforce the learned codes to meet the following criterions: 1) similar images should be encoded into similar binary codes, and vice versa; 2) the quantization loss from Euclidean space to Hamming space should be minimized; and 3) the learned codes should be evenly distributed. The method is further extended into SDHP+ to improve the discriminative power of binary codes. Extensive experimental comparisons with state-of-the-art hashing algorithms are conducted on CIFAR-10 and NUS-WIDE, the MAP of SDHP reaches to 87.67% and 77.48% with 48 b, respectively, and the MAP of SDHP+ reaches to 91.16%, 81.08% with 12 b, 48 b on CIFAR-10 and NUS-WIDE, respectively. It illustrates that the proposed method can obviously improve the search accuracy.
Localizing and recognizing action unit using position information of local feature

Action recognition has attracted much attention for human behavior analysis in recent years. Local spatial-temporal (ST) features are widely adopted in many works. However, most existing works which represent action video by histogram of ST words fail to have a deep insight into a fine structure of actions because of the local nature of these features. In this paper, we propose a novel method to simultaneously localize and recognize action units (AU) by regarding them as 3D (x,y,t) objects. Firstly, we record all of the local ST features in a codebook with the information of action class labels and relative positions to the respective AU centers. This simulates the probability distribution of class label and relative position in a non-parameter manner. When a novel video comes, we match its ST features to the codebook entries and cast votes for positions of its AU centers. And we utilize the localization result to recognize these AUs. The presented experiments on a public dataset demonstrate that our method performs well.
Fast inter frame encoding based on modes pre-decision in H.264

The new video coding standard, H.264 allows motion estimation performing on tree-structured block partitioning and multiple reference frames. This feature improves the prediction accuracy significantly, but the cost of which is the complexity and computation load of video coding increase drastically. The reference software of H.264 adopts a full search scheme and its complexity increases linearly with specified the number of reference frames and the number of modes respectively. In this paper, we propose a fast mode decision algorithm in H.264 which eliminates searching unnecessary modes. The proposed algorithm takes full use of available information obtained from the previous searching process, e.g., macroblock's best mode in searching the previous reference frames. In the algorithm implementation, multiple half-stop conditions have been set in motion estimation so as to decrease encoder's complexity. Simulation results show that this proposed algorithm can effectively reduce complexity of inter-frame encoding, and the quality degradation is tiny compared with full search scheme. Furthermore, our algorithm is adaptive to variant test sequences, no need to set specific experimental threshold.
Optimum bit allocation and rate control for H.264/AVC

For the rate control of H. 264/AVC, one of the most important things is to get the statistics of the current frame accurately. To achieve this, a novel adaptive coding characteristics prediction scheme is presented to improve the accuracy of R-D modeling, by exploiting spatio-temporal correlations. With the proposed prediction scheme, we present a novel rate function and a linear distortion model, and then deduce a simple close-form solution to the problem of optimum bit allocation, just in a TMN-8-alike way. Extensive experiments show that improvements with gains up to 0.92dB per frame over JVT-G012, the current standardized rate control scheme, are achieved by the proposed scheme for a variety of test sequences with less demanding bandwidth.
Compressed Domain Motion Analysis for Video Semantic Events Detection

In this paper, a novel approach is proposed to estimate camera motion and segment moving objects from compressed video streams, aiming to detect semantic events in video clips. Simultaneously using the motion vectors and DC components of MPEG macroblocks (MB), the camera motion type and motion parameters of each frame are estimated with simplified models. Then the segmentation of moving objects is done at macroblock level. Exploring the variation of motion information in consecutive frame sequence, a composite feature can be formed to detect semantic events. The experiment results on TRECVID video corpus show that our approach is very effective and efficient.
Human Attention Model for Action Movie Analysis

Nowadays, automatic detection of highlights for movies is indispensable for video management and browsing. In this paper, we specifically present the formulation of human attention model and its application for action movie analysis. Depending on the relationship between stimuli in both visual and audio modalities and the change of human attention, we construct the visual and audio attention sub-models respectively. By integrating both sub-models, human attention model is formulated and attention flow plot is derived to simulate the change of human attention in the time domain. Based on video structuralization and human attention analysis, we realize action concept annotation for interest-oriented navigation of viewers. The large-scale experiments demonstrate the effectiveness and generality of human attention model for action movie analysis.
Image Search Reranking With Query-Dependent Click-Based Relevance Feedback

Our goal is to boost text-based image search results via image reranking. There are diverse modalities (features) of images that we can leverage for reranking, however, the effects of different modalities are query-dependent. The primary challenge we face is how to fuse multiple modalities adaptively for different queries, which has often been overlooked in previous reranking research. Moreover, multimodality fusion without an understanding of the query is risky, and may lead to incorrect judgment in reranking. Therefore, to obtain the best fusion weights for the query, in this paper, we leverage click-through data, which can be viewed as an “implicit” user feedback and an effective means of understanding the query. A novel reranking algorithm, called click-based relevance feedback, is proposed. This algorithm emphasizes the successful use of click-through data for identifying user search intention, while leveraging multiple kernel learning algorithm to adaptively learn the query-dependent fusion weights for multiple modalities. We conduct experiments on a real-world data set collected from a commercial search engine with click-through data. Encouraging experimental results demonstrate that our proposed reranking approach can significantly improve the NDCG@10 of the initial search results by 11.62%, and can outperform several existing approaches for most kinds of queries, such as tail, middle, and top queries.
A statistical framework for replay detection in soccer video

A novel statistical framework for replay detection is presented in this paper. Unlike current methods, the proposed framework exploits both inherent characters and transition relations of replay and non-replay scenes based on annotation of the video, which realizes segments and classifies video stream into replay and non-replay shots simultaneously. After annotation, the detected replay segment is further verified and its boundaries are adjusted to get more accurate replay segment considering probability distribution of lengths of replay and non-replay shots. Experimental results on soccer video are promising, demonstrating the effectiveness of the proposed framework.
Periodic orbits and chaos in a class of discrete dynamic systems

Regular and chaotic dynamics in an extensive class of discrete dynamical systems in the form x/sub n+1/=/spl mu/x/sub n//sup r/(/spl lambda//sub 1/x/sub n//sup k+/spl alpha//+/spl lambda//sub 2/x/sub n//sup k+/spl alpha/-1/+/spl middot//spl middot//spl middot/+/spl lambda//sub k/x/sub  /spl alpha/+1/) were investigated, and consequently the work of May (1974), Chang (1981) and Hu (1982) had been extended to a general case.
Hollow TV logo detection

Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution . However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for monitoring a specific population of Common Guillemots on Skomer Island, West Wales (UK). We incorporate two types of features, Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP), to capture the edge/local shape information and the texture information of nesting seabirds. Optimal features are selected from a large HOG-LBP feature pool by boosting techniques, to calculate a compact representation suitable for the SVM classifier. A comparative study of two kinds of detectors, i.e., whole-body detector, head-beak detector, and their fusion is presented. When the proposed method is applied to the seabird detection, consistent and promising results are achieved.
Instant Mobile Video Search With Layered Audio-Video Indexing and Progressive Transmission

The proliferation of mobile devices is producing a new wave of applications that enable users to sense their surroundings with smart phones. People are preferring mobile devices to search and browse video content on the move. In this paper, we have developed an innovative mobile video search system through which users can discover videos by simply pointing their phones at a screen to capture a very few seconds of what they are watching. Different than most existing mobile video search applications, the proposed system is aiming at instant and progressive video search by leveraging the light-weight computing capacity of mobile devices. In particular, the system is able to index large-scale video data using a new layered audio-video indexing approach in the cloud, as well as generate lightweight joint audio-video signatures with progressive transmission and perform progressive search on mobile devices. Furthermore, we showcase that the system can be applied to two novel applications-video entity search and video clip localization. The evaluations on the real-world mobile video query dataset show that our system significantly improves user's search experience due to search accuracy, low retrieval latency, and very short recording duration.
Spherical Superpixel Segmentation

These days, superpixel algorithms are widely used in computer vision and multimedia applications. However, existing algorithms are designed for planar images, which are less suited to deal with wide angle images. In this paper, we present a superpixel segmentation method for $\text{360}^\circ$  spherical images. Unlike previous methods, our approach explicitly considers the geometry for spherical images and makes clustering to spherical image pixels. It starts with the seeds defined by Hammersley points sampled on the sphere, then iterates between assignment step and update step, which are both based on the distance metric respecting spherical geometry. We evaluate our method on the transformed Berkeley segmentation dataset and panorama segmentation dataset collected by ourselves. Experimental results show that our method can gain better performance in terms of adherence to image boundaries and superpixel structural regularity. Furthermore, superpixels generated by our method can reserve the coherence across image boundaries and all have closed contours.
Lenselet image compression scheme based on subaperture images streaming

Plenoptic cameras capture the light field in a scene with a single shot and produce lenselet images. From a lenselet image, light field can be reconstructed, with which we can render images with different viewpoints and focal length. Because of large volume data, high efficient image compression scheme for storage and transmission is urgent. Containing 4D light field information, lenselet images have much more redundant information than traditional 2D images. In this paper, we propose a subaperture images streaming scheme to compress lenselet images, in which rotation scan mapping is adopted to further improve compression efficiency. The experiment results show our approach can efficient compress the redundancy in lenselet images and outperform traditional image compression method.
Clustering Guided SVM for Semantic Image Retrieval

SVM (support vector machine) enables effective image classification for semantic image retrieval. However, how to train accurate image classifiers in high-dimensional feature space suffers from the problem of choosing proper training samples. To solve this problem, a novel approach named CGSVM (clustering guided SVM) is presented, which utilizes clustering result to select the most informative image samples to be labeled, and optimize the penalty coefficient. Experimental results show that our algorithm achieves higher search accuracy than regular SVM for semantic image retrieval.
Replay boundary detection in MPEG compressed video

With the large amounts of digital sports video increasing day-by-day, effectively analyzing sports video content has become more and more important. In sports video, replay scene often represents the highlight or interesting event of the video. The detection of replay boundary in the sports video is an important step in analyzing sports video content. In this paper, we present a model of replay boundary detection, and we address a new technique of identifying the replay boundary directly from MPEG compressed domain. It uses MPEG feature including macroblock and motion vector that is easy extract from MPEG video, then applies the rule of macroblock and motion vector to the detection of replay boundary. For working on features directly from the MPEG compressed domain, it performs faster than real time decoding. The experiment results show our model-free method is effective and low complex.
A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors

High Efficiency Video Coding (HEVC) uses a very flexible tree structure to organize coding units, which leads to a superior coding efficiency compared with previous video coding standards. However, such a flexible coding unit tree structure also places a great challenge for encoders. In order to fully exploit the coding efficiency brought by this structure, huge amount of computational complexity is needed for an encoder to decide the optimal coding unit tree for each image block. One way to achieve this is to use parallel computing enabled by many-core processors. In this paper, we analyze the challenge to use many-core processors to make coding unit tree decision. Through in-depth understanding of the dependency among different coding units, we propose a parallel framework to decide coding unit trees. Experimental results show that, on the Tile64 platform, our proposed method achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.
Efficient diode-pumped continuous-wave and picosecond lasers with ceramic Yb:(Y0.9La0.1)2O3 as gain medium

We generated 1.73 W CW laser from a diode pumped ceramic Yb:(Y<inf>0.9</inf>La<inf>0.1</inf>)<inf>2</inf>O<inf>3</inf> laser, corresponding to a slope efficiency of 60.2 %. Mode-locking the laser with SESAM results in 3.1 ps pulse duration at 1075nm.
GPU-based fast scale invariant interest point detector

To take full advantage of the powerful computing capability of graphics processing units (GPU) to speed up local feature detection, we present a novel GPU-based scale invariant interest point detector, coined Harris-Hessian(H-H). H-H detects Harris points in low scale and refines their location and scale in higher scale-space with the determinant of Hessian matrix. Compared to the existing methods, H-H significantly reduces the pixel-level computation complexity and has better parallelism. The experiment results show that with the assistance of GPU, H-H achieves up to a 10–20x speedup than CPU-based method. It only takes 6.3ms to detect a 640 × 480 image with high detection accuracy, meeting the need of real-time detection.
A distribution based video representation for human action recognition

Most current research on human action recognition in videos uses the bag-of-words (BoW) representations based on vector quantization on local spatial temporal features, due to the simplicity and good performance of such representations. In contrast to the BoW schemes, this paper explores a localized, continuous and probabilistic video representation. Specifically, the proposed representation encodes the visual and motion information of an ensemble of local spatial temporal (ST) features of a video into a distribution estimated by a generative probabilistic model such as the Gaussian Mixture Model. Furthermore, this probabilistic video representation naturally gives rise to an information-theoretic distance metric of videos. This makes the representation readily applicable as input to most discriminative classifiers, such as the nearest neighbor schemes and the kernel methods. The experiments on two datasets, KTH and UCF sports, show that the proposed approach could deliver promising results.
Highlights extraction in soccer videos based on goal-mouth detection

A new method is proposed for highlight extraction in soccer videos based on goal-mouth detection. This approach is based on the observations that the appearance of goal-mouth points to a high likelihood of exciting action in soccer videos and that highlight is composed of certain types of scene views which exhibit certain transition rules. To exploit those observations, first goal-mouth are detected and segmented in soccer videos by Top-Hat Transform and some domain rules, and then with scene transition rules highlight is extracted based on goal-mouth detection. The effectiveness and efficiency of this approach are demonstrated by the experimental results on shot detection.
The Most Efficient Tile Size in Tile-Based Cylinder Panoramic Video Coding and its Selection Under Restriction of Bandwidth

Cylinder panoramic videos are a 360deg representation in horizontal of a certain scene. The users can navigate interactively through the scene and change their view angels. Because of large field of view, panoramic videos are often high-resolution and consume a significant amount of bandwidth for transmission. To resolve the problem, tile-based panoramic video coding and transmission is applied in some systems. With tile-based panoramic video coding and transmission, the panorama is divided into tiles and only the tiles involved with perspective view are transmitted and decoded. In the tile-based video coding, the selection of tile size is a problem. Different tile sizes will bring different quality of perspective view with the same bandwidth. In this paper, we proposed a method to select the most efficient tile size. With this method, the most efficient tile size could be fast selected and users can build the best quality perspective view from cylinder panoramic videos with the given bandwidth.