nils-herrmann/clean_scirepeval huggingface dataset:
Codebook optimization using word activation forces for scene categorization

Visual codebook based quantization of robust appearance descriptors extracted from local image patches is an effective means of capturing image statistics for texture analysis and natural scene classification. In this paper, based on the newly proposed statistics of word activation forces (WAFs), we optimize the codebook. Currently, codebooks are typically created from a set of training images using a clustering algorithm. However, these codebooks are often functionally limited due to redundancy. We show that WAFs can remove the redundancy efficiently. In the experiment, the proposed method achieved the state-of-the-art performance on the Caltech-101, fifteen natural scene categories and VOC2007 databases. The optimization method also offers insights into the success of several recently proposed images classification approaches, including vector quantization (VQ) coding in the Spatial Pyramid Matching (SPM), sparse coding SPM (ScSPM), and Locality-constrained Linear Coding (LLC).
Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay

This paper proposes a bio-driven algorithm that detects cell regions automatically in the human embryonic stem cell (hESC) images obtained using a phase contrast microscope. The algorithm uses both statistical intensity distributions of foreground/hESCs and background/substrate as well as cell property for cell region detection. The intensity distributions of foreground/hESCs and background/substrate are modeled as a mixture of two Gaussians. The cell property is translated into local spatial information. The algorithm is optimized by parameters of the modeled distributions and cell regions evolve with the local cell property. The paper validates the method with various videos acquired using different microscope objectives. In comparison with the state-of-the-art methods, the proposed method is able to detect the entire cell region instead of fragmented cell regions. It also yields high marks on measures such as Jacard similarity, Dice coefficient, sensitivity and specificity. Automated detection by the proposed method has the potential to enable fast quantifiable analysis of hESCs using large data sets which are needed to understand dynamic cell behaviors.
Detection of non-dynamic blebbing single unattached Human Embryonic Stem Cells

Human Embryonic Stem Cells (HESCs) are promising for the treatment of many diseases and for toxicological testing. There is a great interest among biologists to automatically determine the number of various types of cells in a population of mixed morphologies. This study addresses quantification of non-dynamic blebbing single unattached human embryonic stem cells (NDBSU-HESCs) that are in suspension and do not show evidence of blebbing. Current image processing methods are inadequate for detecting these cells in real time. In this paper, we propose a method for NDBSU-HESC detection by using multiple trained classifiers where each classifier eliminates cells with properties unmatched to NDBSU-HESCs. The paper validates the method with many videos captured with live stem cells.
Ethnicity classification based on gait using multi-view fusion

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy Image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best
Efficient Recognition of Highly Similar 3D Objects in Range Images

Most existing work in 3D object recognition in computer vision has been on recognizing dissimilar objects using a small database. For rapid indexing and recognition of highly similar objects, this paper proposes a novel method which combines the feature embedding for the fast retrieval of surface descriptors, novel similarity measures for correspondence and a support vector machine (SVM)-based learning technique for ranking the hypotheses. The local surface patch (LSP) representation is used to find the correspondences between a model-test pair. Due to its high dimensionality, an embedding algorithm is used that maps the feature vectors to a low-dimensional space where distance relationships are preserved. By searching the nearest neighbors in low dimensions, the similarity between a model-test pair is computed using the novel features. The similarities for all model-test pairs are ranked using the learning algorithm to generate a short list of candidate models for verification. The verification is performed by aligning a model with the test object. The experimental results, on the UND dataset (302 subjects with 604 images) and the UCR dataset (155 subjects with 902 images) that contain 3D human ears, are presented and compared with the geometric hashing technique to demonstrate the efficiency and effectiveness of the proposed approach.
A new semi-supervised EM algorithm for image retrieval

One of the main tasks in content-based image retrieval (CBIR) is to reduce the gap between low-level visual features and high-level human concepts. This paper presents a new semi-supervised EM algorithm (NSSEM), where the image distribution in feature space is modeled as a mixture of Gaussian densities. Due to the statistical mechanism of accumulating and processing meta knowledge, the NSS-EM algorithm with long term learning of mixture model parameters can deal with the cases where users may mislabel images during relevance feedback. Our approach that integrates mixture model of the data, relevance feedback and long term learning helps to improve retrieval performance. The concept learning is incrementally refined with increased retrieval experiences. Experiment results on Corel database show the efficacy of our proposed concept learning approach.
Facial emotion recognition with anisotropic inhibited Gabor energy histograms

This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the effectiveness of the DVP descriptor in both the face and the palmprint recognition tasks under uncontrolled illuminations. State-of-the-art approaches have yet to deliver a feature representation for facial emotion recognition that can be applied to non-trivial unconstrained, continuous video data sets. Initially, research advanced with the use of Gabor energy filters. However, in recent work more attention has been given to other features. Gabor energy filters lack generalization needed in unconstrained situations. Additionally, they result in an undesirably high feature vector dimensionality. Nontrivial data sets have millions of samples; feature vectors must be as low dimensional as possible. We propose a novel texture feature based on Gabor energy filters that offers generalization with a background texture suppression component and is as compact as possible due to a maximal response representation and local histograms. We improve performance on the non-trivial Audio/Visual Emotion Challenge 2012 grandchallenge data set.
Robust visual rear ground clearance estimation and classification of a passenger vehicle

Computation of Visual Rear Ground Clearance of vehicles is an important computer vision application. This problem is challenging as the road and vehicle rear bumper may have subtle appearance differences, vehicle motion is on uneven surfaces and there are real-time considerations. In this paper a method is presented to compute the Visual Rear Ground Clearance of a vehicle from its rear view video and classify it into two classes namely Low Visual Rear Ground Clearance Vehicles and High Visual Rear Ground Clearance Vehicles. A multi-frame matching technique in conjunction with geometry based constraints is developed. It detects Regions-of-Interest ROIs of moving vehicles and moving shadows, and uses shape constraints associated with vehicle geometry as viewed from its rear. It tracks stable features on a vehicle to compute the Visual Rear Ground Clearance. The results are shown on a large dataset of videos and compared against the ground-truth.
Human Embryonic Stem Cell Detection by Spatial Information and Mixture of Gaussians

Human Embryonic Stem Cells (HESCs) possess the potential to provide treatments for cancer, Parkinson's disease, Huntington's disease, Type 1 diabetes mellitus etc. Consequently, HESCs are often used in the biological assay to study the effects of chemical agents in the human body. However, detection of HESC is often a challenge in phase contrast images. To improve the accuracy of HESC colony detection, we combine spatial information and the outcome of a mixture of Gaussians model. While a mixture of Gaussians generates reasonable labels for various regions of HESC images, it lacks spatial details and connectivity. Sets of spatially consistent candidate labeling are generated by median filtering the image at different scales followed by thresholding. An optimal combination of filter scale and threshold which maximizes the correlation coefficient between the spatial information and the mixture of Gaussians output is obtained. The paper validates the method for various HESC videos.
Visual Learning by Evolutionary and Coevolutionary Feature Synthesis

In this paper, we present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically at the genotype level. The training coevolves feature extraction procedures, each being a sequence of elementary image processing and computer vision operations applied to input images. Extensive experimental results show that the approach attains competitive performance for three-dimensional object recognition in real synthetic aperture radar imagery.
Learning integrated online indexing for image databases

Most of the current image retrieval systems use "one-shot" queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithms is used where weights measuring feature importance along input dimensions remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. We present a novel method that enables image retrieval procedures to continuously learn feature relevance based on user's feedback, and which is highly adaptive to query locations. Experimental results are presented that provide the objective evaluation of learning behaviour of the method for image retrieval.
Adaptive object detection from multisensor data

This paper focuses on developing self-adapting automatic object detection systems to achieve robust performance. Two general methodologies for performance improvement are first introduced. They are based on optimization of parameters of an algorithm and adaptation of the input to an algorithm. Different modified Hebbian learning rules are used to build adaptive feature extractors which transform the input data into a desired form for a given object detection algorithm. To show its feasibility, input adaptors for object detection are designed and tested using multisensor data including SAR, FLIR, and color images. Test results are presented and discussed in the paper.
Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments.
Reinforcement learning for combining relevance feedback techniques

Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user's feedback history. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. We propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions also provides significant contributions for improvement. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model against a growing-size database.
Context-aware reinforcement learning for re-identification in a video network

Re-identification of people in a large camera network has gained popularity in recent years. The problem still remains challenging due to variations across cameras. A variety of techniques which concentrate on either features or matching have been proposed. Similar to majority of computer vision approaches, these techniques use fixed features and/or parameters. As the operating conditions of a vision system change, its performance deteriorates as fixed features and/or parameters are no longer suited for the new conditions. We propose to use context-aware reinforcement learning to handle this challenge. We capture the changing operating conditions through context and learn mapping between context and feature weights to improve the re-identification accuracy. The results are shown using videos from a camera network that consists of eight cameras.
Adaptive image segmentation using genetic and hybrid search methods

This paper describes an adaptive approach for the important image processing problem of image segmentation that relies on learning from experience to adapt and improve the segmentation performance. The adaptive image segmentation system incorporates a feedback loop consisting of a machine learning subsystem, an image segmentation algorithm, and an evaluation component which determines segmentation quality. The machine learning component is based on genetic adaptation and uses (separately) a pure genetic algorithm (GA) and a hybrid of GA and hill climbing (HC). When the learning subsystem is based on pure genetics, the corresponding evaluation component is based on a vector of evaluation criteria. For the hybrid case, the system employs a scalar evaluation measure which is a weighted combination of the different criteria. Experimental results for pure genetic and hybrid search methods are presented using a representative database of outdoor TV imagery. The multiobjective optimization demonstrates the ability of the adaptive image segmentation system to provide high quality segmentation results in a minimal number of generations.<<ETX>>
Extraction of Blebs in Human Embryonic Stem Cell Videos

Blebbing is an important biological indicator in determining the health of human embryonic stem cells (hESC). Especially, areas of a bleb sequence in a video are often used to distinguish two cell blebbing behaviors in hESC: dynamic and apoptotic blebbings. This paper analyzes various segmentation methods for bleb extraction in hESC videos and introduces a bio-inspired score function to improve the performance in bleb extraction. Full bleb formation consists of bleb expansion and retraction. Blebs change their size and image properties dynamically in both processes and between frames. Therefore, adaptive parameters are needed for each segmentation method. A score function derived from the change of bleb area and orientation between consecutive frames is proposed which provides adaptive parameters for bleb extraction in videos. In comparison to manual analysis, the proposed method provides an automated fast and accurate approach for bleb sequence extraction.
Generic object recognition using CAD-based multiple representations

Real-world applications of computer vision usually involve a variety of object models making a single model representation somewhat inadequate for object recognition. Multiple representations, on the other hand, allow different matching strategies to be applied for the same object, or even for different parts of the same object. The paper is concerned with the use of CAD-derived hierarchical models having multiple representations/spl minus/concave/convex edges and straight homogeneous generalized cylinder/spl minus/for generic object recognition in outdoor visible imagery. The authors also present a refocused matching algorithm that uses a hierarchically structured model database to facilitate generic object recognition. Experimental results demonstrating generic recognition of objects in perspective, aerial images are presented.<<ETX>>
Closed-loop object recognition using reinforcement learning

Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of color images with varying external conditions.
Image super-resolution by extreme learning machine

Image super-resolution is the process to generate high-resolution images from low-resolution inputs. In this paper, an efficient image super-resolution approach based on the recent development of extreme learning machine (ELM) is proposed. We aim at reconstructing the high-frequency components containing details and fine structures that are missing from the low-resolution images. In the training step, high-frequency components from the original high-resolution images as the target values and image features from low-resolution images are fed to ELM to learn a model. Given a low-resolution image, the high-frequency components are generated via the learned model and added to the initially interpolated low-resolution image. Experiments show that with simple image features our algorithm performs better in terms of accuracy and efficiency with different magnification factors compared to the state-of-the-art methods.
Spatio-temporal pattern recognition of dendritic spines and protein dynamics using live multichannel fluorescence microscopy

Actin-regulating proteins, such as cofilin, are essential in regulating the shape of dendritic spines, and synaptic plasticity in both neuronal functionality as well as in neurodegeneration related to aging. The analysis of the motility of cofilin in fluorescence video-microscopy allows the discovery of its effects on cell functions. However, the flow of cofilin has not been analyzed to date by automatic means. This paper presents a novel automated pattern recognition system to analyze protein trafficking in neurons. Using spatio-temporal information present in multichannel fluorescence videos, the system generates a temporal maximum intensity projection that enhances the signal-to-noise ratio of important biological structures, segments and tracks dendritic spines, and quantifies the flux and density of proteins in spines. The temporal dynamics of spines is used to generate spine energy images which are used to automatically classify the shape of dendritic spines as stubby, mushroom, or thin. By tracking these spines over time and using their intensity profiles, the system is able to analyze the flux patterns of cofilin and other fluorescently stained proteins. The cofilin flux patterns is found to be correlated with the dynamically changing dendritic spine shapes. The results are presented using multichannel fluorescence videos.