nils-herrmann/clean_scirepeval huggingface dataset:
Pattern colour separable image coding for content based indexing

A colour image coding system based on the pattern colour separable (PCS) theory of human colour vision is developed. The PCS image coding system codes an image using three separate channels: pattern, colour and strength. The pattern channel corresponds to the visual pathway that is sensitive to spatial frequency information, the colour channel corresponds to the visual pathway that is sensitive to the colour information and the strength channel corresponds to the visual pathway that is sensitive to the strength of the visual stimulus. Decoding of the image is based on a neural network trained in a self-organizing manner. Compression performances of the system are shown to be very good. Query by image contents is a successful approach to image database indexing and retrieval. Because image data take up huge amount of storage space and network bandwidth, compressing the image is a standard practice. Given that images are compressed/coded in a database, it mould be more efficient if the compressed data can also be directly used as indices for content-based query. We show that the contents (shapes and colours) of the images are embedded in the compressed data of the PCS system and therefore can be readily and efficiently used as indices for content based image indexing.
A classification method for estimating the illuminant of an image

Identifying the light source of an image is important for image processing tasks such as colour correction and white point balancing. This is also known as colour constancy in computer vision. This paper presents a novel clustering classification colour constancy framework (the 4C method). Based on the assumption that similar illuminants will result in similar white point colours, we first use a clustering algorithm to group similar white point colours of the training samples into the same cluster. We then treat the images in the same cluster as belonging to the same illumination source and each cluster as one class of illuminants. The colour constancy problem, i.e., that of estimating the unknown illuminant of an image, becomes that of identifying which illuminant class (cluster) the images illuminant falling into. To achieve this, we derive an effective colour feature representation of the image and use a general classification algorithm to classify the image into one of the illuminant classes (clusters). We present experimental results on publicly available testing datasets and show that our new method is competitive to state of the art.
Illuminant classification based on random forest

We present a novel machine learning/pattern recognition based colour constancy method. We cast colour constancy as an illumination source recognition problem, and have developed an effective and efficient random forest based classification technique for inferring the class of illumination source of an image. In an opponent colour space, we have developed a binary image representation feature that is somewhat insensitive to image contents for building the random forest classifier that infers the likely class of the illumination source of the image. The binary image feature and the tree structure of the recognition system are intrinsically efficient. We present results on colour constancy benchmark data sets and show that our new technique outperforms state of the art techniques.
Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn relationships between attributes (physical and immunological) and the resulting tumour stage and survival. Results for conventional machine learning approaches can be considered poor, especially for predicting tumour stages for the most important types of cancer. This poor performance is further investigated and compared with a synthetic, dataset based on the logical exclusive-OR function and it is shown that there is a significant level of “anti-learning” present in all supervised methods used and this can be explained by the highly dimensional, complex and sparsely representative dataset. For predicting the stage of cancer from the immunological attributes, anti-learning approaches outperform a range of popular algorithms.
Robust Detection of Region-Duplication Forgery in Digital Image

Region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image in order to conceal an important object in the scene, is one of the common image forgery techniques. In this paper, we describe an efficient and robust algorithm for detecting and localizing this type of malicious tampering. We present experimental results which show that our method is robust and can successfully detect this type of tampering for images that have been subjected to various forms of post region duplication image processing, including blurring, noise contamination, severe lossy compression, and a mixture of these processing operations
Color image coding, indexing and retrieval using binary space partitioning tree

This paper presents a unified approach to colour image coding, content-based indexing, and retrieval for database applications. The binary space partitioning (BSP) tree, traditionally used in gray scale image coding (Wu 1992, Radha et al. 1996) is extended to represent colour images. The BSP tree, hence the structure, of the image is explicitly coded. A method is developed to compute the similarities of images based on their BSP tree representations. In image database applications, the images in the database are coded by BSP tree to achieve a good balance between storage efficiency and easy manipulation of image data. Content-based image querying is performed in the compressed bit streams by comparing the BSP tree of the query image with those of the images in the database.
Improvement and evaluation of visual saliency based on information theory

Visual saliency is a well-known image processing technique based on theory about visual attention of human beings. Several proposed computing approaches based on findings in psychological and neurological researches about the part of human brain area in charge of visual attention. Contrast to those methods, in this paper the information theory is recommended as backbone for a new approach of building visual attention computer model, assumed that the uniqueness of a pixel or a group of pixels correlates with saliency. The information saliency is identified in both spatial domain and temporal domain, and eventually those two pieces of information is combined in a mathematical integrated framework naturally. Experiments of the model on intensity, contrast, and especially full motion video show that its performance is comparable to other state-of-art saliency approaches. Though there are still minor flaws in the information based saliency, it is a potential alternative approach for visual attention in video-based applications.
From Local Pixel Structure to Global Image Super-Resolution: A New Face Hallucination Framework

We have developed a new face hallucination framework termed from local pixel structure to global image super-resolution (LPS-GIS). Based on the assumption that two similar face images should have similar local pixel structures, the new framework first uses the input low-resolution (LR) face image to search a face database for similar example high-resolution (HR) faces in order to learn the local pixel structures for the target HR face. It then uses the input LR face and the learned pixel structures as priors to estimate the target HR face. We present a three-step implementation procedure for the framework. Step 1 searches the database for example faces that are the most similar to the input, and then warps the example images to the input using optical flow. Step 2 uses the warped HR version of the example faces to learn the local pixel structures for the target HR face. An effective method for learning local pixel structures from an individual face, and an adaptive procedure for fusing the local pixel structures of different example faces to reduce the influence of warping errors, have been developed. Step 3 estimates the target HR face by solving a constrained optimization problem by means of an iterative procedure. Experimental results show that our new method can provide good performances for face hallucination, both in terms of reconstruction error and visual quality; and that it is competitive with existing state-of-the-art methods.
Near-Duplicate Image Detection in a Visually Salient Riemannian Space

This paper presents a framework for near-duplicate image detection in a visually salient Riemannian space. A visual saliency model is first used to identify salient regions of the image and then the salient region covariance matrix (SCOV) of various image features is computed. SCOV, which lies in a Riemannian manifold, is used as a robust and compact image content descriptor. An efficient coarse-to-fine Riemannian (CTOFR) image search strategy has been developed to improve efficiency while maintaining accuracy. CTOFR first uses a computationally fast but less accurate log-Euclidean Riemannian metric to do a coarse level search of the entire database and retrieve a subset of likely targets and then uses a computationally expensive but more accurate affine-invariant Riemannian metric to search the returns from the coarse search. We present experimental results to demonstrate that SCOV is a very compact, robust, and discriminative descriptor which is competitive to other state-of-the-art descriptors for near-duplicate image and video detection. We show that CTOFR can yield significant speedups over traditional full search methods without sacrificing accuracy, and that the larger the database the higher the speedup factor.
A New Normalization Approach for Combining Similarities

There are many scenarios when there is a need to combine different similarities together. It is well known that one should normalize the similarity values before combining them. Common used normalization techniques including normalizing the mean and variance. In this paper, we proposed that besides normalizing the mean and variance, it is sometimes necessary to normalize their value histogram. We employ histogram matching technique, which is a common used technique in image process area, to achieve this. Both toy examples and real-world experiments have confirmed the validity of the proposed approach.
Post-processing block coded images using artificial neural networks

A technique employing artificial neural networks for post-processing block coded images is presented. Visually important image features are extracted from the decompressed image and used as input to a feedforward neural network. The neural network learns to reconstruct the difference image between the original (uncompressed) and the decompressed image. Coding artifact reduction is achieved by adding the neural networks output to the decompressed image. Experimental results using the new technique for post-processing quadtree coded images are presented. It is shown the new technique can significantly improve the compressed image both in terms of peak signal to noise ratio (PSNR) and visual quality of the image.
A multicurve tone mapping operator for the display of high dynamic range image and video

This paper presents a novel method for tone mapping high dynamic range images and videos for display in conventional low dynamic range devices. The new multi-curve method uses tone mapping curves that are adaptive to local luminance levels. The new method has the computational simplicity of curve based tone mapping methods and achieves results comparable to spatial operator methods. When applying the operator to render high dynamic range video, we introduce a bilateral style temporal filtering which successfully avoids undesirable shot noise and ghosting effects. It is also shown that our HDR tone mapping operator is a useful image enhancement method for standard format (8 bits/pixel) images. (7 pages)
Deep Feature Consistent Variational Autoencoder

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.
Learning sample subspace with application to face detection

We present a novel maximum correlation sample subspace method and apply it to human face detection (Yang, M-H, Kriegman, D, and Ahuja, N, January 2002) in still images. The algorithm starts by projecting all the training samples onto each sample and selects the sample with the largest accumulated projection as the first subspace base vector. After a base vector is selected, all other samples are made orthogonal to the current base vector and which is in turn used to form the training samples for learning the next base vector. Each subspace base is created by a one-pass process and therefore the method is computationally very efficient. These bases form a transform and we use it to derive discriminative features for face detection by training a support vector machine classifier. We perform testing on both CMU and MIT face detection image data sets. Extensive experiments demonstrate that our results are comparable to those published in state of the art literature.
MLP for adaptive postprocessing block-coded images

A new technique based on the multilayer perceptron (MLP) neural network is proposed for blocking-artifact removal in block-coded images. The new method is based on the concept of learning-by-examples. The compressed image and its original uncompressed version are used to train the neural networks. In the developed scheme, inter-block slopes of the compressed image are used as input, the difference between the original uncompressed and the compressed image is used as desired output for training the networks. Blocking-artifact removal is realized by adding the neural network's outputs to the compressed image. The new technique has been applied to process JPEG compressed images. Experimental results show significant improvements in both visual quality and peak signal-to-noise ratio. It is also shown the present method is comparable to other state of the art techniques for quality enhancement in block-coded images.
Crowd density estimation based on rich features and random projection forest

Current state of the art crowd density estimation methods are based on computationally expensive Gaussian process regression or Ridge regression models which can only handle a small number of features. In many computer vision applications, it has been empirically shown that a richer set of image features can lead to enhanced performances. In this paper, we reason that using more image features could potentially boost the performances of crowd detection and thus propose to employ much extensive and richer feature sets for crowd density estimation. To achieve computational efficiency and scalability, we use random forest as the regression model whose tree structure is intrinsically fast and scalable. Unlike traditional approaches to random forest construction, we embed random projection in the tree nodes to simultaneously combat the curse of dimensionality and to introduce randomness in the tree construction (we call this Random Projection Forest) thus making our new method very efficient and effective. Experimental results on two public pedestrian detection video databases show that our new method achieves state of the art performances that are superior to those of previously published regression techniques.
Tuberculosis bacteria detection based on Random Forest using fluorescent images

Tuberculosis (TB) is an infectious disease in low and middle-income countries. There are many methods of physical examinations for tuberculosis detection, but the most effective method is visual examination using microscopes, including fluorescent microscopy and bright field microscopy. However, according to the analysis of previous research work, the method based on fluorescent microscopes can yield on average 10% on sensitiveness than the bright field microscopy. In this paper, we present a TB detection method based on Random Forest using fluorescent microscopic images. We have conducted experiments on three types of classifiers, in terms of Random Forest (RF), linear SVM (LinSVM), and Cross-Validation SVM (CVSVM). The experimental results show that the machine learning method of Random Forest for TB segmentation and detection using fluorescent images has obtained better performance than other two methods.
A statistical image of colour space

The distribution of colours in an image has proven to be very useful for object recognition. Building on Swain's colour indexing (1991), 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. In this paper we look at colour distribution based recognition from a rather novel image processing perspective. Specifically we view the distribution of colours in an image as an image and so recast colour distribution matching as a problem of image comparison. Two results are reported here. First that, by compressing images we can improve matching efficiency (recognize objects more quickly). Second, that the degree of compression (and so speedup) that is possible depends on the colour space on top of which the distribution images are built. The more uniform opponent colour encoding can be compressed more effectively compared with conventional rg-chromaticity encoding. We explain this in the following way: image processing is based on the assumption that all image locations are equal so, to treat colour space as an image, each colour location should also be equally likely. This is in fact approximately the case for the opponent chromaticity space. To validate our approach we repeated Swain's object recognition experiments. We show that the distribution of colours in an image (which Swain encoded with 4096 numbers) represented by 8 numbers (the projection coefficients onto an 8-dimensional principal component basis) suffices to achieve the same (almost perfect) recognition rate. Our method delivers a 500-fold speed up in indexing without loss of accuracy. This result scales to a second larger database of 140 images.
A Novel Polar Space Random Field Model for the Detection of Glandular Structures

In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundary of a gland. Next, we develop a visual feature-based support vector regressor to verify if the detected contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. Our approach can not only detect the existence of the gland, but also can accurately locate it with pixel accuracy. In the experiments, we treat the task of detecting glandular structures as object (gland) detection and segmentation problems respectively. The results indicate that our new technique outperforms state-of-the-art computer vision algorithms in respective fields.
Automated prediction of shopping behaviours using taxi trajectory data and social media reviews

The Huff model is a well used mathematical abstraction for predicting shopping centre patronage. It considers two factors: shopping centre attractiveness, and customers' travel costs. Here, taxi trajectory data (more than three million journeys) and social media data (more than eight thousand customer reviews) is used to calibrate the Huff model for five primary shopping centres in the rapidly expanding metropolitan city of Shenzhen, China. The Huff model is calibrated in two ways: globally, to find the single pair of best-fit parameters for attractiveness and travel cost; and locally, using Geographical Weighted Regression to find the best-fit parameters at each spatial location. Results demonstrate that customer reviews on social media provide relatively high prediction accuracy for weekend shopping behaviours when the Huff model is calibrated globally. In contrast, customer footfall, calculated directly from number of taxi journeys, provides higher prediction accuracy when the Huff model is calibrated locally. This suggests that, at weekends, sensitivity to footfall has greater spatial variance (i.e., customers living in some areas have greater preference for shopping at popular centres) than sensitivity to customer reviews (i.e., regardless of where customers live, positive reviews on social media are equally likely to affect behaviour). We present this geographical homogeneity in review sensitivity and heterogeneity in footfall sensitivity as a novel discovery with potential applications in urban, retail, and transportation planning.
Nonlinear adaptive processing of block coded images

An adaptive nonlinear scheme for the blocking effect reduction in low bit rate block coded images is presented. A method is developed to extract useful features from the compressed image and uses them as inputs to three-layer feedforward neural networks. The neural networks learn to reduce the coding errors in the block border areas. The new technique has been applied to process JPEG compressed images and results are presented which show improvements in both visual quality and peak-signal to noise ratio (PSNR).
A hierarchical algorithm for image multi-labeling

This paper presents an efficient two-stage method for multi-class image labeling. We first propose a simple label-filtering algorithm (LFA), which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then apply the Naive-Bayes Nearest-Neighbor (NBNN) classifier as the second stage of our algorithm to identify the labels for the query image. This approach has been evaluated on the Corel database, and compared to existing algorithms. Experiment results show that our proposed algorithm can achieve a promising result, as it outperforms existing algorithms.
A progressively predictive image pyramid for efficient lossless coding

A low entropy pyramidal image data structure suited for lossless coding and progressive transmission is proposed in this work. The new coder, called the progressively predictive pyramid (PPP) is based on the well-known Laplacian pyramid. By introducing inter-resolution predictors into the original Laplacian pyramid, we show that the entropy level in the original pyramid can be reduced significantly. To take full advantage of progressive transmission, a scheme is introduced to create the predictor adaptively, thus eliminating the need to transmit the predictor and reducing the coding overheads. A method for designing the predictor is presented. Numerical results show that PPP is superior to traditional approaches to pyramid generation in the sense that the pyramids generated by PPP always have significantly lower entropy values.