nils-herrmann/clean_scirepeval huggingface dataset:
Fast and accurate Nearest Neighbor search in the manifolds of symmetric positive definite matrices

In this paper, we present a fast and accurate Nearest Neighbor (NN) search method in the Riemannian manifolds formed by a kind of structured data - symmetric positive definite (SPD) matrices. We use an ensemble of vocabulary trees based on hierarchical k-means clustering and query these trees to find the NN candidates in sub-linear time. As generating these vocabulary trees with widely used affine-invariant Riemannian metric (AIRM) will be very time-demanding, we propose to use the second-order approximation to AIRM (SOA-AIRM). We evaluate the proposed NN search algorithm in the application scenario of near-duplicate image detection in a large database. Experimental results demonstrate that the proposed method significantly outperforms state of the art techniques in terms of both accuracy and speed.
Recent advances in high dynamic range imaging technology

Recently, visual representations using high dynamic range (HDR) images become increasingly popular, with advancement of technologies for increasing the dynamic range of image. HDR image is expected to be used in wide-ranging applications such as digital cinema, digital photography and next generation broadcast, because of its high quality and its powerful expression ability. HDR imaging technologies will spread its sphere of influence in imaging industry. In this paper, we review the state-of-the-art studies and the trends of the HDR imaging, in terms of the following three points: (1) HDR imaging sensor and HDR image generation techniques as image acquisition technologies, (2) encode method of HDR images for efficient transmission and storage, (3) human visual system issues associated with reproduction of HDR image.
Novel histogram processing for colour image enhancement

In practice, the histogram equalization often produces images with unnatural appearances and visually disturbing artefacts. One of the reasons for these unwanted effects is that the histogram equalization attempts to force the output image to have a uniform pixel distribution regardless of what the original image's pixel distribution may be. In this paper, we present a novel histogram processing algorithm which takes into account the original image's pixel distribution in the equalization process. The method uses a single parameter to control the degree of contrast enhancement to ensure that the output have an enhanced appearance which is also faithful to that of the original image and is free of unwanted visually disturbing artefacts. We first develop the algorithm for the luminance channel and then extend the method to the colour components. We present experimental results to demonstrate the better performances of our new method over established methods in the literature.
Gabor-like eigenfilters for texture segmentation

Classification, segmentation and discrimination of textures are some of the most fundamental tasks in many image understanding, pattern recognition and machine vision applications. In the past two decades or so, these topics have been actively studied by many researchers and different approaches to the problem have been proposed. These approaches normally operates on a relatively small neighbourhood and therefore are most effective in segmenting the class of so called microtextures. The paper presents the framework and a method for designing the Gabor (1946) like eigenfilter, demonstrates its effectiveness in texture segmentation and provides some experimental results.
Example selective and order independent learning-based image super-resolution

In this paper, we present a novel example selective and order independent method for learning-based image super-resolution. We first present a method that selectively utilizes training samples according to the content of the input image. Experimental results show that by selecting the training samples appropriately, it is possible to dramatically reduce the computational costs without degrading image quality. We then present a new order independent technique that is shown to perform better than traditional order dependent techniques in learning image super-resolution and can also be applied to image editing such as region filling and object removal from images.
A Novel Method for Detecting Cropped and Recompressed Image Block

One of the most common practices in image tampering involves cropping a patch from a source and pasting it onto a target. In this paper, we present a novel method for the detection of such tampering operations in JPEG images. The lossy JPEG compression introduces inherent blocking artifacts into the image and our method exploits such artifacts to serve as a 'watermark' for the detection of image tampering. We develop the blocking artifact characteristics matrix (BACM) and show that, for the original JPEG images, the BACM exhibits regular symmetrical shape; for images that are cropped from another JPEG image and re-saved as JPEG images, the regular symmetrical property of the BACM is destroyed. We fully exploit this property of the BACM and derive representation features from the BACM to train a support vector machine (SVM) classifier for recognizing whether an image is an original JPEG image or it has been cropped from another JPEG image and re-saved as a JPEG image. We present experiment results to show the efficacy of our method.
Constructing colour image databases using BTC for efficient storage and effective retrieval

We introduce a simple image coding method, the block truncation coding (BTC) technique, as a novel approach to the construction of colour image databases. It is shown that BTC can not only be used to compress images, thus achieving storage efficiency, but the BTC codes can also be used directly to construct image features for effective image retrieval. From the BTC code we have developed an image feature termed the BTC colour co-occurrence matrix (BCCM) as an effective measure of image contents. Experimental results are presented to show that BCCM is comparable to state of the art techniques, such as color correlogram, in image retrieval.
Scene structural matrix for image indexing and retrieval

We present a new image indexing and retrieval method for image database applications. We introduce a novel image content description feature termed scene structural matrix (SSM). The SSM captures the overall structural information of the scene by indexing the geometric features of the image. We also introduce a constraint adaptive image segmentation method termed middle cut (MC) to partition the image and derive the multi-scale geometric structures of the image. Experimental results show that SSM is particularly effective in retrieving images with strong structural features, such as landscape photographs. It is also shown that SSM is robust against spatial and spectral distortions thus making it superior to the traditional colour histogram, colour correlogram and MPEG7-color structure histogram in certain applications.
Appearance indexing

Although it is very hard to quantify the visual impression of an image, we conjecture that the overall visual appearance of an image may be the combined effect of the appearances of image patches of various shapes and sizes. We can imagine that all possible appearances of image patches of all possible shapes and sizes form a conceptual appearance space. Each point in the appearance space therefore corresponds to a certain combination of the object parameters (shape, size, surface texture, pose, orientation, etc) and the imaging conditions (illuminating source, viewing angle, sensor response etc.). Using real sensor data and unsupervised learning, statistically most representative appearance prototypes can be found to approximate the appearance space. Statistics of these appearance prototypes present in an image therefore characterize the image content, which in turn can be used to perform tasks such as content-based image retrieval.
Learning a discriminative sparse tri-value transform

Simple binary patterns have been successfully used for extracting feature representations for visual object classification. In this paper, we present a method to learn a set of discriminative tri-value patterns for projecting high dimensional raw visual inputs into a low dimensional subspace for tasks such as face detection. Unlike previous methods that use predefined simple transform bases to generate tens of thousands features first and then use machine learning to select the most useful features, our method attempts to learn discriminative transform bases directly. Since it would be extremely hard to develop analytical solutions, we define an objective function that can be solved using simulated annealing. To reduce the search space, we impose sparseness and smoothness constraints on the transform bases. Experimental results demonstrate that our method is effective and provides an alternative approach to effective visual object classification.
Improving Video Coding at Scene Cuts using Attention based Adaptive Bit Allocation

Existing video coding methods can cause visual quality and buffer occupancy to fluctuate significantly at scene cuts. To address this problem, we have developed a novel visual attention based adaptive bit allocation method. We first perform scene cut detection to extract frames in the vicinities of dramatic scene changes; we then perform visual saliency analysis on those frames to grade the macro-blocks according to their visual importance; and finally we devise a visual attention based adaptive bit allocation scheme which assigns more bits to visually salient blocks and fewer bits to visually less important blocks. We will present experimental results which demonstrate that at scene cut areas, coding quality in terms of PSNR of our method are both higher and much smoother than those of existing coding methods and the buffer occupancy of our method is also much more consistent and has less fluctuation. Our method is compatible with other rate control schemes and can be easily implemented to improve existing video coding standards such as MPEG-2, H.264/AVC, and others.
Color by linear neighborhood embedding

This paper presents a new semiautomatic method for adding colors to monochrome images. The new method, termed color by linear neighborhood embedding (CLNE) first computes the geometry of local image patches in the monochrome image and then transmits the geometry to the chrominance. Following computational methods of the locally linear embedding (LLE) algorithm, we compute the geometry of the monochrome image by solving a constrained least squares problem and embed the computed geometry in the chrominance images by solving a linearly constrained quadratic optimization problem, both in closed forms. We also show that the computed geometry of different color channels are highly correlated thus empirically supporting methods that embed the geometry of the monochrome in the chrominance for colorization.
HEP-2 cells classification via novel object graph based feature and random forest

Human Epithelial type 2 (HEp-2) cells are the most common substrates for anti-nuclear antibodies detection. Traditional manual diagnosis heavily depends on the experience of histopathologists, which is time consuming and subject to subjective mistakes. With the recent progress of digital scanners and dramatic development in computer vision techniques, computer-aided diagnosis has now become achievable. In this paper a novel automatic system is proposed to classify the HEp2 cell images into six categories. Along with a set of local gradient based textural descriptors, we introduce a novel objectbased method to decompose the binary image into primitive objects and represent them with a set of morphological features. Random forest is then applied for classification. The advantages of this system are as following: (1) robustness against the changes of intensity and rotation, (2) more discriminative information compared to normal morphological descriptors. We evaluate the proposed approach using the publicly available ICPR 2012 datasets. The experimental results show that the proposed method achieves comparable performance with the state-of-the-art methods.
From sensory coding to scene classification

We present a storage-efficient and computationally fast method for rapid navigation/browsing through large image repositories and for content-based image retrieval. In the developed system, multiple resolution, orientation achromatic, and opponent chromatic channels are sequentially encoded by a maximal information sensory encoding model, which conveniently and effectively indexes the images into a binary tree data structure. Content-based image retrieval, database navigation, and image browsing are done very efficiently and rapidly by manipulating the n-bit binary keys in the binary tree data structure. We present experimental results to demonstrate the effectiveness of our method.
Image indexing using compressed colour histograms

The distribution of colours in an image has proven to be very useful for object recognition. Building on Swain's colour indexing, colour distributions are now an integral part of many recognition schemes. This is not to say that colour alone suffices but rather that colour is one important cue that aids recognition. Colour indexing is performed on colour distribution histograms, and as such, the speed of the system is directly related to the size of the histogram to be indexed. It is shown how colour histograms can be effectively compressed and how compressed colour histograms can be compared for indexing. The authors make two important contributions. First, they show that an opponent colour histogram can be compressed more readily than can conventional colour space. Secondly, they use the standard transform encoding methods (the Karhunen-Loeve transform, the discrete cosine transform, the Hadamard transform and hybrid transforms) to compress colour histograms. Experiments show that compression rates of up to 250:1 are possible without affecting indexing performance. This means that a database can be searched that is 250 times larger in the same time as that searched by conventional indexing.
Recovering High Dynamic Range Radiance Maps from Photographs Revisited: A Simple and Important Fix

An influential technique introduced by Debevec and Malik has become the de facto standard for recovering high dynamic range radiance maps from photographs and has been widely used in research and commercial systems for over a decade. However, we have discovered an important defect in the original algorithm that will make this technique often fail to produce reasonable results in the extremely bright or dark regions of a scene. In this paper, we introduce a novel technique to correct this defect. Instead of the original algorithm where only pixel values from the photographs are used to guide the synthesis of the high dynamic range radiance map, we explicitly incorporate the shutter speed information of the camera. At each spatial pixel location, we estimate a "suitable shutter" that will make that location best exposed. A pixel's contribution to the high dynamic range radiance value is not only a function of its value but also depends on the difference between shutter speed used to take the pixel and the estimated "suitable shutter" of that pixel. We also show that this new idea can be successfully used to directly fuse differently exposed photographs into a single low dynamic range image for display in conventional low dynamic range devices.
Image and feature co-clustering

The visual appearance of an image is closely associated with its low-level features. Identifying the set of features that best characterizes the image is useful for tasks such as content-based image indexing and retrieval. In this paper, we present a method which simultaneously models and clusters large sets of images and their low-level visual features. A computational energy function suited for co-clustering images and their features is first constructed and a Hopfield model based stochastic algorithm is then developed for its optimization. We apply the method to cluster digital color photographs and present results to demonstrate its usefulness and effectiveness.
2309 skin conditions and crowd-sourced high-level knowledge dataset for building a computer aided diagnosis system

Worldwide, it is believed that there are between 1000 to 2000 skin conditions of which 20% are difficult to diagnose. An intelligent computer-aided diagnosing system not only helps patients with no or little access to health services but also can benefit typical general practitioners, who have received minimal dermatology training. We have built a challenging dataset containing 2309 images from 44 different skin conditions. As most state-of-the-art visual recognition techniques fail to perform on this dataset, we provide crowd-sourced high-level knowledge of these images to enable the development of a “human in the loop” approach. This achieves higher recognition rates by combining visual features, and human provided information. The high-level knowledge was obtained through employing 361 “Amazon Mechanical Turk” workers (non-medical experts), who answered a set of predefined perceptual questions that represents the humans' understanding of lesions in the images. We have made the images, and the high-level knowledge information publicly available. In this paper, we present the data, methods of using it, and performances of three baseline techniques. This paper and its associated data will be very useful to facilitate the development of computer aided medical diagnostic techniques.
Fast tone mapping for high dynamic range images

We present a fast, effective and flexible tone reproduction method that preserves visibility and contrast impression of high dynamic range scenes in low dynamic range reproduction devices. A single parameter controls the visibility and contrast in a simple and elegant manner and at interactive speed. The new method is simple to use and is computationally highly efficient. Experiments show that the technique produces good results on a variety of high dynamic range images. The method can also be used to enhance ordinary low dynamic range digital images.
A Semi-automatic Image Analysis Tool for Biomarker Detection in Immunohistochemistry Analysis

Digitized tissue slide analysis allows the Pathologists to use computer assisted image analysis technology to reduce time cost and increase the accuracy of diagnosis. In this paper, we present an ease to use semi-automatic tool which can detect and separate stains in tissue samples correctly. This statistical model based tool has been applied to detecting different kinds of stains. Multiple evaluation processes are implemented to demonstrate the robustness, accuracy and usefulness of this tool. Experimental results show that the tool performs significantly better than established popular methods such as color deconvolution and CMYK in stained color separation.
Saliency Modulated High Dynamic Range Image Tone Mapping

This paper presents a new high dynamic range image tone mapping technique - saliency modulated tone mapping (SMTM). The HDR image is not directly viewable and dynamic range compression will unavoidably loose information. A saliency map analyzes the visual importance of the regions and can therefore direct the tone mapping operators to preserve the visual conspicuity of the regions that should more likely attract visual attention. In SMTM, we have developed a very fast algorithm to first compute the visual saliency map of the high dynamic range radiance map and then directly use the saliency of the local regions to control the local tone mapping curve such that highly salient regions will have their details and contrast better protected so as to remain salient and attract visual attention in the tone mapped display. We present experimental results to show that SMTM provides competitive performances to state of the art tone mapping techniques in rending visually pleasing low dynamic range displays. We also show that SMTM is better able to preserve the visual saliency of the HDR image and that SMTM renders high saliency regions to stand out to attract observers attention.
Object specific deep feature and its application to face detection

We present a method for exploiting object specific deep features and use face detection as a case study. We seek to discover and exploit the convolutional channels of a CNN in which neurons are activated by the presence of specific objects in the input image. A method for explicitly fine-tuning a pre-trained CNN to induce an object specific channel (OSC) and systematically identifying it for the human face object has been developed. Building on the basic OSC features, we introduce a multi-scale approach to constructing robust face heatmaps for rapidly filtering out non-face regions thus significantly improving search efficiency for face detection in unconstrained settings. We show that multi-scale OSC can be used to develop simple and compact face detectors with state of the art performance.
Learning deep semantic attributes for user video summarization

This paper presents a Semantic Attribute assisted video SUMmarization framework (SASUM). Compared with traditional methods, SASUM has several innovative features. Firstly, we use a natural language processing tool to discover a set of keywords from an image and text corpora to form the semantic attributes of visual contents. Secondly, we train a deep convolution neural network to extract visual features as well as predict the semantic attributes of video segments which enables us to represent video contents with visual and semantic features simultaneously. Thirdly, we construct a temporally constrained video segment affinity matrix and use a partially near duplicate image discovery technique to cluster visually and semantically consistent video frames together. These frame clusters can then be condensed to form an informative and compact summary of the video. We will present experimental results to show the effectiveness of the semantic attributes in assisting the visual features in video summarization and our new technique achieves state-of-the-art performance.
Skin detection using spatial analysis with adaptive seed

This paper introduces a new method for adaptive skin detection in color images combined with spatial analysis of skin pixels. It has been reported in many works that adaptation of a skin color model to a particular image may decrease the false positives, however the false negatives are considerably high unless a local model is combined with the global one. Another possibility for improvement is to analyze spatial properties of the pixels classified as skin, but this operation strongly depends on the seed extraction technique. Our contribution lies in using a local dynamic skin model learned from the detected faces to extract seeds for the spatial analysis. We present an extensive experimental study confirming that our method outperforms alternative skin detection techniques.
Precise multi-level face detector for advanced analysis of facial images

This study presents a new method for precise detection of frontal human faces and eyes using a multi-level ellipse detector combined with a support vector machines verifier. Main contribution of this study lies in improving the accuracy of eye detection in high-quality images, which is often neglected by alternative methods. Although many approaches to face detection have been proposed recently, relatively little attention has been paid to the detection precision. It is worth noting that the detection precision is particularly important for face analysis purposes. More specifically, the authors demonstrate that the detection error propagation substantially affects the face recognition performance. With the proposed improvements the authors have managed to increase the face recognition rate by 7.7% for AR database compared with the publicly-available implementation of the well-established Viola–Jones face and eye detector.