nils-herrmann/clean_scirepeval huggingface dataset:
Efficient motion estimation for video coding in wireless surveillance applications

In this paper, we propose a novel approach to perform selective motion estimation specific to video coding for wireless surveillance applications. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Two approaches for selective motion estimation, group of pictures (GOP) based and frame based, are implemented. In the former, motion estimation is performed for the whole group of pictures only when moving object is detected in any frame of the GOP. While for the latter approach; each frame is tested for the motion activity and consequently for selective motion estimation. Experimental evaluation shows that significant reduction in computational complexity can be achieved by applying the proposed strategy. The achieved efficiency gains make the proposed approach suitable for low bitrate transmission in wireless applications.
Fusion of semantic and visual information for automatic indexing and annotation of key frame images

Modern database structures consisting of audio visual and low-level semantic data are in line with the needs and requirements of end user customers. It is difficult to expect the end user to provide any high level input rather than a very general key word annotation of the recorded data. A typical user scenario appearing in modern multimedia deals with having just audio-visual data with no additional knowledge. How to effectively annotate and enable efficient indexing and categorisation that would subsequently provide easy browsing and retrieval of desired content is the problem addressed in this paper.
Error Robustness Scheme for Scalable Video Based on the Concatenation of LDPC and Turbo Codes

In this paper, a novel approach for transmission of scalable video over wireless channel is proposed. The proposed approach jointly optimises the bit allocation between a wavelet-based scalable video coding framework and a forward error correction codes. The forward error correction codes is based on the serial concatenation of LDPC codes and turbo codes. Turbo codes shows good performance at high error rates region but LDPC outperforms turbo codes at low error rates. So the concatenation of LDPC and TC enhances the performance at both low and high signal to noise ratios. The scheme minimizes the reconstructed video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the source rate distortion characteristics and the statistics of the available codes. Furthermore, an efficient decoding algorithm is proposed. Experimental results clearly demonstrate the superiority of the proposed approach over conventional forward error correction techniques.
Combining K-means and semivariogram-based grid clustering

Clustering is useful in several situations, amongst others: data mining, information retrieval, image segmentation, and data classification. In this paper an approach for grouping data sets that are indexed in the space is proposed. It is based on the k-means algorithm and grid clustering. The former is the simplest and most commonly used clustering technique. A major problem with this algorithm is that it is sensitive to the selection of the initial partition. The latter is commonly used for grouping data that are indexed in the space. The goal in this paper is to overcome the high sensitivity of the k-means algorithm to the starting conditions by using the available spatial information. A semivariogram-based grid clustering is introduced. It uses the spatial correlation for determining the bin size. Since the bins are constrained to regular blocks while the spatial distribution of objects is not regular, we propose to combine this technique with a conventional k-means algorithm. By using the semivariogram an excellent initialization of the k-means is provided. Experimental results show that the final partition preserves the spatial distribution of the objects
Horizontal flip-invariant sketch recognition via local patch hashing

This paper introduces a flip aware patch matching frame-work that facilitates scalable sketch recognition. An overlapping spatial grid is utilized to generate an ensemble of patches for each sketch. We rank similarities between freely drawn sketches via a spatial voting process where similar patches in terms of shape and structure arbitrate for the result. Patch similarity is efficiently estimated via the min-hash algorithm. A novel spatial aware reverse index structure ensures the scalability of our scheme. We show the benefits of horizontal flip invariance and structural information in sketch recognition and demonstrate state-of-the-art results in two challenging sketch datasets.
Multi-scale reflection invariance

In this position paper, we consider the state of computer vision research with respect to invariance to the horizontal orientation of an image - what we term reflection invariance. We describe why we consider reflection invariance to be an important property and provide evidence where the absence of this invariance produces surprising inconsistencies in state-of-the-art systems. We demonstrate inconsistencies in methods of object detection and scene classification when they are presented with images and the horizontal mirror of those images. Finally, we examine where some of the invariances are exhibited in feature detection and descriptors, and make a case for future consideration of reflection invariance as a measure of quality in computer vision algorithms.
Peer-to-peer streaming of scalable video in future Internet applications

Scalable video delivery over peer-to-peer networks appears to be key for efficient streaming in emerging and future Internet applications. Contrasting the conventional server-client approach, here, video is delivered to a user in a fully distributed fashion. This is, for instance, beneficial in cases where a high demand for a particular video content is imposed, as different users can receive the same data from different peers. Furthermore, due to the heterogeneous nature of Internet connectivity, the content needs to be delivered to a user through networks with highly varying bandwidths. Moreover, content needs to be displayed on a variety of devices featuring different sizes, resolutions, and computational capabilities. If video is encoded in a scalable way, it can be adapted to any required spatio-temporal resolution and quality in the compressed domain, according to a peer bandwidth and other peers¿ context requirements. This enables efficient low-complexity content adaptation and interoperability for improved peer-to-peer streaming in future Internet applications. An efficient piece picking and peer selection policy enables high quality of service in such a streaming system.
Layout Methods for Intuitive Partitioning of Visualization Space

In this paper we address two relevant tasks in image visualisation research: layout methods for presenting content and relations within image databases; and optimal solutions for efficient use of the entire display space. We introduce a novel approach to enable users searching on large image archives to distinguish heterogeneous sets of images. Thus, helping them to navigate or browse image databases according to relevant query directions. Two methods for mapping similarity relations between images and cognitive partitioning of the display space are presented.
An Efficient Joint Source-Channel Coding for Wavelet Based Scalable Video

A robust and efficient approach for scalable video transmission over wireless channels is presented. The proposed approach jointly optimizes source and channel coding in order to minimize the overall end-to-end distortion. In particular, the forward error correction method based on turbo codes is considered. Aiming at improving the overall performance of the underlying joint source-channel coding, the combination of the channel coding rate, interleaver and packet size are optimized for turbo codes subject to a constraint on the overall transmission bitrate budget. Experimental results show that the proposed approach outperforms conventional forward error correction techniques at all bit error rates, even in very adverse conditions.
Noise removal from polygonal shapes using combined inverse diffusion filter and triangle method

We introduce a method for de-noising, segmenting and measuring similarity of shapes. The proposed method consists of two techniques. First we use an inverse diffusion filter for enhancement of relevant polygonal vertices and removal of noise and irrelevant edges. Second we apply a technique that removes the vertices that form the triangles with the smallest areas. In a thorough experimental evaluation, the combined method shows successful noise suppression while preserving dominant vertices present in the input shape.
Double-Binary Turbo Code for Transmission of Scalable Video Bitstream

A novel low bit rate video transmission scheme is proposed for the communication of scalable video bitstream over wireless channel. The schema consists of motion compensated t+2D wavelet decomposition scalable coder and double binary turbo code. Double binary codes were used for protection since they perform better than classical turbo coders in terms of better convergence for iterative decoding, a large minimum distance and computational cost. An efficient method to estimate the minimum distance of double binary turbo codes is also proposed. The best packet length are evaluated by measuring the minimum distance of turbo code to adopt the scalable bitstream with unequal error protection. Simulation results show that the unequal error protection is quite efficient, even in very adverse conditions, and it clearly outperforms simple FEC techniques
Knowledge-Based Image Processing for Classification and Recognition in Surveillance Applications

This short paper serves as preface for the ICIP special session on knowledge-based image processing for classification in surveillance applications. This special session presents work on integrative research aimed at automatic classification and semantic-based recognition of scenes and events for surveillance applications. The focus is integrative research on low-level multimedia analysis, knowledge extraction and semantic analysis. The eight papers selected for the special session target convergence of these fields by integrating, for a purpose, what can be disparate disciplines. The integration covers image processing, knowledge representation, information retrieval and semantic analysis. The targeted application scenario involves the processing of input data captured by video-cameras where security is of interest. The work presented in this special session has originated in two large international cooperative projects funded by the European commission under the sixth framework programme of the Information Society Technology (1ST). The mandate and scope of these two projects is very generic embracing several applications that rely on technology for bridging the gap between low-level content descriptions that can be computed automatically by a machine and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media (i.e. the semantic gap). However, the section presented in these conference proceedings is restricted to research work undertaken under more limited scenarios for specific video-based surveillance.
A game theoretic framework for optimal resource allocation in P2P scalable video streaming

In this paper we describe a game theoretic framework for scalable video streaming over a peer-to-peer network. The proposed system integrates optimal resource allocation functionalities with an incentive provision mechanism for data sharing. First of all, we introduce an algorithm for packet scheduling that allows users to download a specific sub-set of the original scalable bit-stream, depending on the current network conditions. Furthermore, we present an algorithm that aims both at identifying free-riders and minimising transmission delay. Uncooperative peers are cut out of this system, while users upload more data to those which have less to share, in order to fully exploit the resources of all the peers. Experimental evaluation shows that this model can effectively cope with free-riders and minimise transmission delay for scalable video streaming.
Mode decision with enhanced inter-prediction in HEVC

The HEVC standard makes use of inter-prediction to exploit temporal redundancy in order to obtain efficient compression. A novel approach is proposed in this paper where the inter-prediction for a coding unit (CU) is further enhanced by means of simple parametric transformations. The Enhanced Inter-Prediction (EIP) module is embedded within the mode-decision module at a CU level, to obtain a more accurate inter-prediction and hence possibly reducing the residuals before transform and quantisation. The EIP parameters are encoded in the CU header, and the exact rate-distortion (RD) cost after reconstruction is computed to make sure that the EIP is only used when it is effective. The approach is also improved by means of efficient quantisation of the EIP parameters. The optimal quantisation steps for each CU are found following from analytical considerations as illustrated in the paper. Results show consistent improvements over conventional HEVC encoding in terms of PSNR and bitrate performance.
Aspect coherence for graph-based semantic image labelling

In image semantic segmentation a semantic category label is associated to each image pixel. This classification problem is characterised by pixel dependencies at different scales. On a small-scale pixel correlation is related to object instance sharing, whereas on a middle- and large scale to category co-presence and relative location constraints. The contribution of this study is two-fold. First, the authors present a framework that jointly learns category appearances and pixel dependencies at different scales. Small-scale dependencies are accounted by clustering pixels into larger patches via image oversegmentation. To tackle middle-scale dependencies a conditional random field (CRF) is built over the patches. A novel strategy to exploit local patch aspect coherence is used to impose an optimised structure in the graph to have exact and efficient inference. The second contribution is a method to account for full patch neighbourhoods without introducing loops in the graphical structures. ‘Weak neighbours’ are introduced, which are patches connected in the image but not in the inference graph. They are pre-classified according to their visual appearance and their category distribution probability is then used in the CRF inference step. Experimental evidence of the validity of the method shows improvements in comparison to other works in the field.
Empirical Analysis of Libs Images for Ovarian Cancer Detection

To develop a way to detect early epithelial ovarian cancer we expose blood samples to a laser. Using Laser induced breakdown spectroscopy, (LIBS) plasma images of the blood samples are generated and analyzed. In this paper we compare the images from blood specimens of cancer free mice to those of transgenic mice; that is, mice that are currently developing epithelial ovarian cancer and are approximately the same age as the disease free mice. Our ultimate goal is to look for changes in the shape and edges of the image that might indicate the presence of cancer. To analyze the images we use a nonlinear diffusion filter for enhancement of relevant image edges and removal of noise and irrelevant texture.
Context awareness in graph-based image semantic segmentation via visual word distributions

This paper addresses the problem of image semantic segmentation (or semantic labelling), that is the association of one of a predefined set of semantic categories (e.g. cow, car, face) to each image pixel. We adopt a patch-based approach, in which super-pixel elements are obtained via oversegmentation of the original image. We then train a Conditional Random Field on heterogeneous descriptors extracted at different scales and locations. This discriminative graphical model can effectively account for the statistical dependence of neighbouring patches. For the more challenging task of considering long-range patch dependency and contextualisation, we propose the use of a descriptor based on histograms of visual words extracted in the vicinity of each patch at different scales. Experiments validate our approach by showing improvements with respect to both a base model not using distributed features and the state of the art works in the area.
Dimensionality reduction for content-based image classification

Effective ways of organizing image descriptors is a critical design step of content-based image classification systems. Suitable descriptors are selected according to the problem domain for generating the feature space. Using several descriptors improves accuracy of representation but risen some challenges such as non linear combination, expensive computation and the curse of dimensionality. In This work an approach using a non parametric statistical test for effective dimensionality reduction is presented. The proposed method facilitates feature discrimination and keeps relevant information.
Multi-feature fusion in image clustering using ant-inspired methods

Clustering of visual data is necessary for its effective organisation, summarisation and retrieval. In this paper, the appropriateness of biologically inspired models to tackle this problem is discussed and suitable strategies to solve this specific image processing task are derived. The proposed techniques are inspired by the optimal movements of ants and their biologically optimised colony behaviour. In the first proposal, the problem of multi-feature fusion using relevant, yet different, discriminative low-level features is tackled by Ant Colony Optimisation and its learning mechanism. In the second proposal, another metaheuristic model is applied. Here, the ability of ants to build live structures with their bodies is used in order to discover, in a distributed and unsupervised way, a tree-structured organisation of images. Finally, the proposed techniques are comprehensively evaluated and selected representative results are reported.
A hierarchical approach for low-access latency image indexing and retrieval

A major problem to be faced when efficient schemas for image indexing and retrieval are envisaged is the large workload and high complexity of underlying image-processing algorithms. The research leading to this paper focused on this problem, i.e. efficiency and scalability. A novel representation of color histograms is presented. The introduced model is based on hierarchical image adaptive color histograms. One important aspect of the envisaged hierarchic definition is that details at various levels of scale can be obtained. For fast search of similar features in large databases large sets of non-similar images are discriminated at very low computational cost using low detailed descriptions. The search is then progressively refined until only a few very similar objects or images are found and ranked using higher levels of detail. To assess the performance of the approach several experiments have been conducted. Selected results are reported.
Multi-generation packet scheduling for live streaming with network coding

Over the last decade, the emergence of new multimedia devices has motivated the research on efficient media streaming mechanisms that can adapt to dynamic network conditions and heterogeneous devices' capabilities. Network coding (NC) as a rateless code has been applied to collaborative media streaming applications and brings substantial improvements regarding throughput and delay in collaborative media streaming applications. However, little attention has been given to the recoverability of encoded data, especially for the streaming with a strict deadline. When a receiver obtains insufficient packets, it is impossible for receivers to recover any original packets. This in turn leads to severe quality of experience. In this paper, we solve the unrecoverable transmission by determining a scalable layer subscription and scheduling strategy. This scalable layer subscription algorithm is treated as a video quality maximization problem in multiple generations. Then this problem is solved using a dynamic programming algorithm. Experimental results confirm that the proposed algorithm brings better data recoverability and a better quality of service in terms of better video quality, delivery ratio, lower redundancy rate, and significantly lower superfluous video packet rate under different network sizes than conventional random-push schemes.
Lagrange Multiplier Selection for 3-D Wavelet Based Scalable Video Coding

In this paper a thorough analysis on the theoretical rate distortion model and the rate distortion performance in an open-loop structure is conducted. A Lagrange multiplier selection for 3D wavelet based scalable video coding is then derived. The proposed Lagrange multiplier is adaptive with respect to the characteristics of video content. Furthermore, it is especially suitable for 3-D wavelet based scalable video coding where quantisation steps are unavailable. Extensive experimental results have demonstrated the effectiveness of the proposed Lagrange multiplier selection.
Scene-Oriented Hierarchical Classification of Blurry and Noisy Images

A system for scene-oriented hierarchical classification of blurry and noisy images is proposed. It attempts to simulate important features of the human visual perception. The underlying approach is based on three strategies: extraction of essential signatures captured from a global context, simulating the global pathway; highlight detection based on local conspicuous features of the reconstructed image, simulating the local pathway; and hierarchical classification of extracted features using probabilistic techniques. The techniques involved in hierarchical classification use input from both the local and global pathways. Visual context is exploited by a combination of Gabor filtering with the principal component analysis. In parallel, a pseudo-restoration process is applied together with an affine invariant approach to improve the accuracy in the detection of local conspicuous features. Subsequently, the local conspicuous features and the global essential signature are combined and clustered by a Monte Carlo approach. Finally, clustered features are fed to a self-organizing tree algorithm to generate the final hierarchical classification results. Selected representative results of a comprehensive experimental evaluation validate the proposed system.
From mid-level to high-level: Semantic inference for multimedia retrieval

The problem of bridging the semantic gap can be approached by dividing all types of metadata extracted from multimedia content into three levels — low, mid and high — according to their levels of semantic abstraction and try to define the mapping between them. This paper proposes a scheme for extracting high-level semantic information out of mid-level features, which can be applied in dealing with highly semantic queries in image retrieval. Mid-level features used in this research contain some level of semantic meaning but are not directly useful in real retrieval scenarios. However, they usually have strong relationships to high-level queries but these relationships are often ignored due to their implicitness. The aim of the proposed approach is to explore hidden interrelationships between mid-level features and the high-level query terms, by learning a Bayesian network model from a small amount of training data. Semantic inference and reasoning is then carried out based on the learned Bayesian network model, in order to decide whether a video is relevant to a high-level query. The extracted high-level semantic terms can be annotated on the video content for future retrieval. Two experimental scenarios were considered in this paper and the experiments on RUSHES videos have produced satisfactory results.