nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Retrieval from Large-Scale Egocentric Visual Data Using a Sparse Graph Representation

We propose representing one's visual experiences (captured as a series of ego-centric videos) as a sparse-graph, where each node is an individual frame in the video, and nodes are connected if there exists a geometric transform between them. Such a graph is massive and contains millions of edges. Autobiographical egocentric visual data are highly redundant, and we show how the graph representation and graph clustering can be used to exploit redundancy in the data. We show that popular global clustering methods like spectral clustering and multi-level graph partitioning perform poorly for clustering egocentric visual data. We propose using local density clustering algorithms for clustering the data, and provide detailed qualitative and quantitative comparisons between the two approaches. The graph-representation and clustering are used to aggressively prune the database. By retaining only representative nodes from dense sub graphs, we achieve 90% of peak recall by retaining only 1% of data, with a significant 18% improvement in absolute recall over naive uniform subsampling of the egocentric video data.
Cascaded classification with optimal candidate selection for effective place recognition

A two-stage cascaded classification approach with an optimal candidate selection scheme is proposed to recognize places using images taken by camera phones. An optimal acceptance threshold is chosen to maximize the probability of accepting more positives and rejecting more negatives at the first stage so that an optimal number of candidates are selected. The first classifier is trained using simple color and texture features. The second classifier is trained by scale invariant feature transform (SIFT). For a query image, a number of matching candidates are selected using k nearest neighbor at the first stage and passed on to the second stage for a refining classification to select the best matching result. The searching range is narrowed down dynamically at the second stage depending on the output of the first stage. Experimental results show that this method is promising by improving the recognition accuracy and reducing the computation time.
Automatic macula detection from retinal images by a line operator

This paper presents an automatic macula detection technique that makes use of the circular brightness profile of the macula: the macula is usually darker than the surrounding pixels whose intensities increase gradually with their distances from the macula center. A line operator is designed to capture the macula circular brightness profile, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/ maximum variation has specific patterns that indicate the position of the macula efficiently. The proposed technique has been tested over DRIVE project's dataset and the STARE project's dataset. Experiments show that the accuracies reach up to 100% and 95.45%, respectively, based on 35 and 44 retinal images having discernible macula within the two public datasets.
A Wearable Virtual Usher for Vision-Based Cognitive Indoor Navigation

Inspired by progresses in cognitive science, artificial intelligence, computer vision, and mobile computing technologies, we propose and implement a wearable virtual usher for cognitive indoor navigation based on egocentric visual perception. A novel computational framework of cognitive wayfinding in an indoor environment is proposed, which contains a context model, a route model, and a process model. A hierarchical structure is proposed to represent the cognitive context knowledge of indoor scenes. Given a start position and a destination, a Bayesian network model is proposed to represent the navigation route derived from the context model. A novel dynamic Bayesian network (DBN) model is proposed to accommodate the dynamic process of navigation based on real-time first-person-view visual input, which involves multiple asynchronous temporal dependencies. To adapt to large variations in travel time through trip segments, we propose an online adaptation algorithm for the DBN model, leading to a self-adaptive DBN. A prototype system is built and tested for technical performance and user experience. The quantitative evaluation shows that our method achieves over 13% improvement in accuracy as compared to baseline approaches based on hidden Markov model. In the user study, our system guides the participants to their destinations, emulating a human usher in multiple aspects.
Automatic Optic Disc Detection From Retinal Images by a Line Operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.
Medical-Image Retrieval Based on Knowledge-Assisted Text and Image Indexing

Voluminous medical images are generated daily. They are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical-image databases, both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework based on support vector machines to facilitate modular design and learning of medical semantics from images. We present two complementary visual indexing approaches within this framework: a global indexing to access image modality and a local indexing to access semantic local features. Two fusion approaches are developed to improve textual retrieval using the UMLS-based image indexing. First, a simple fusion of the textual and visual retrieval approaches is proposed, improving significantly the retrieval results of both text and image retrieval. Second, a visual modality filtering is designed to remove visually aberrant images according to the query modality concept(s). Using the ImageCLEFmed database, we demonstrate the effectiveness of our framework which is superior when compared with the automatic runs evaluated in 2005 on the same medical-image retrieval task.
Multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy images

This paper presents a novel multi-level approach for bleeding detection in Wireless Capsule Endoscopy (WCE) images. In the low-level processing, each cell of K×K pixels is characterized by an adaptive color histogram which optimizes the information representation for WCE images. A Neural Network (NN) cell-classifier is trained to classify cells in an image as bleeding or non-bleeding patches. In the intermediate-level processing, a block which covers 3×3 cells is formed. The intermediate-level representation of the block is generated from the low-level classifications of the cells, which captures the spatial local correlations of the cell classifications. Again, a NN block-classifier is trained to classify the blocks as bleeding or non-bleeding ones. In the high-level processing, the low-level cell-based and intermediate-level block-based classifications are fused for final detection. In this way, our approach can combine the low-level features from pixels and intermediate-level features from local regions to achieve robust bleeding detection. Experiments on real WCE videos have shown that the proposed method of multi-level classification is not only accurate in both detection and localization of potential bleedings in WCE images but also robust to complex local noisy features.
Lens image registration for cataract detection

Cataract is the leading cause of blindness worldwide. In this paper, registration of retroillumination image is investigated for detection of cortical and posterior subcapsular cataract (PSC). The pupil region is detected by local entropy filtering. The anterior image and posterior image are registered using maximum normalized cross-correlation. The registration of anterior and posterior image can be used to detect opacity in the future. The proposed algorithms were tested using clinical data and the experimental results indicate that the proposed method could facilitate automatic cataract diagnosis.
Principal curvature of point cloud for 3D shape recognition

In the recent years, we experienced the proliferation of sensors for retrieving depth information on a scene, such as LIDAR or RGBD sensors (Kinect). However, it is still a challenge to identify the meaning of a specific point cloud to recognize the underlying object. Here, we wonder if it is possible to define a global feature for an object that is robust to noise, sampling and occlusion. We propose a local measure based on curvature. We called it Principal Curvature because rather than using the Gaussian curvature we keep the information of the two principal curvatures. In our approach, this local information is then aggregated as histograms that are compared with a Chi-2 metric. Results show the robustness of the method particularly when only few points are available. This means that our approach can be very suitable to match objects even with a limited resolution and possible occlusions. It could be particularly adapted to recognize objects with LIDAR inputs.
Image indexing and retrieval using visual keyword histograms

We propose a novel image representation called visual keyword histogram (VKH) for content-based indexing and retrieval. Visual keywords are domain-relevant visual prototypes (e.g. faces, foliage, buildings etc) with both perceptual appearance and textual semantics. Collectively, VKHs axe computed over spatial tessellation to represent the distribution of visual keywords in various parts of an image. To construct a vocabulary of visual keywords, an incremental neural network is deployed to learn visual keywords from examples. This allows us to build domain-specific visual vocabularies rapidly and incrementally. Last but not least, we propose a new visual query language called Query by Spatial Icons (QBSI) that allows a user to specify a query in terms of "what" and "where". A visual query term constrains whether a visual keyword should be present and a query formals chains these terms into a disjunctive normal form via logical operators. We show our approach on real and complex home photos with very promising results.
Hidden Maximum Entropy Approach for Visual Concept Modeling

Recently, the bag-of-words approach has been successfully applied to automatic image annotation, object recognition, etc. The method needs to first quantize an image using the visual terms and then extract the image-level statistics for classification. Although successful applications have been reported, it lacks the capability to model the spatial dependency and the correspondence between the patches and visual parts. Moreover, quantization deteriorates the descriptive power of patch feature. This paper proposes the hidden maximum entropy (HME) approach for modeling visual concepts. Each concept is composed of a set of visual parts, each part having a Gaussian distribution. The spatial dependency and image-level statistics of parts are modeled through the maximum entropy. The model is learned using the developed EM-IIS algorithm. We report the preliminary results on the 260 concepts in the Corel dataset and compared with the maximum entropy (ME) approach. Our experiments on concept detection show that (1) a relative increment of 10.3% is observed when comparing the average AUC value of HME approach with that of the ME approach and (2) the HME approach reduces the average equal error rate from 0.412 for the ME approach to 0.354.
Timetable scheduling using neural networks with parallel implementation on transputers

A scheduling neural network based on the interactive activation and competition model, integrating into the conventional sequential scheduling framework to perform timetable scheduling is proposed. Parallel implementation of the model of a transputer system is realized by task decomposition so that independent groups of classes are scheduled by independent transputers and conflicts are resolved via message passing on priorities of classes. Compared to the Hopfield network-based optimization approach, the size of the scheduling network considered grows at a slower rate (linear) with the problem size and is flexible for encoding more realistic constraints.<<ETX>>
Selecting representative and distinctive descriptors for efficient landmark recognition

To have a robust and informative image content representation for image categorization, we often need to extract as many as possible visual features at various locations, scales and orientations. Thus it is not surprised that an image has a few hundreds or even thousands of visual descriptors. This raises huge cost of computation and memory. To eliminate the problem, we can only select the most representative and distinctive descriptors and discard the other non-informative features when training the image category models. This paper will present a Markov chain based algorithm to learn a measure of the descriptor importance in order to weigh the degree of representativeness and distinctiveness. From the measures the descriptor selection algorithm is derived. The presented approach starts from constructing a graph with each node being a descriptor to characterize the pair-wise descriptor similarity and then the PageRank algorithm is exploited to estimate the stationary distribution of the graph whose values are the indicator of the descriptor importance. We evaluate the proposed approach on the STOIC-101 landmark dataset. Our experiments demonstrate the Markov chain based descriptor selection can select the most informative descriptors to distinguish the landmarks. Even with the large reduction of the size of descriptors, the classification accuracy is still competitive or overcomes compared with the system without any descriptor selection.
An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy

The study of stem cells is one of the most important biomedical research. Understanding their development could allow multiple applications in regenerative medicine. For this purpose, automated solutions for the observation of stem cell development process are needed. This study introduces an on-line analysis method for the modelling of neurosphere evolution during the early time of their development under phase contrast microscopy. From the corresponding phase contrast time-lapse sequences, we extract information from the neurosphere using a combination of phase contrast physics deconvolution and curve detection for locate the cells inside the neurosphere. Then, based on prior biological knowledge, we generate possible and optimal 3-dimensional configuration using 2D to 3D registration methods and evolutionary optimisation algorithm.
Automatic working area classification in peripheral blood smears without cell central zone extraction

In this paper we study automatic classification of working areas in peripheral blood smears using image analysis and recognition methods. Such automatic classification can provide objective and reproducible quality control for the evaluation of smears and smear maker devices. However, research in this filed has drawn little attention. Existing methods either can not differentiate correctly different cell distributions or rely on the extraction of the central pallor zones in cells for counting, which are not always observable. In contrast, we do not rely on the pallor zone extraction thus on more general basis. We introduce two generic parameters to measure the goodness of working areas, one for the degree of overlap, and the other for the spatial occupancy. We also propose a cascading classification network for the classification of different areas. The effectiveness of our method has been tested on over 150 labeled images acquired from three malaria-infected Giemsa-stained blood smears using an oil immersion 100× objective.
Similarity Learning for Nearest Neighbor Classification

In this paper, we propose an algorithm for learning a general class of similarity measures for kNN classification. This class encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. The algorithm we propose is an extension of the voted perceptron algorithm and allows one to learn different types of similarity functions (either based on diagonal, symmetric or asymmetric similarity matrices). The results we obtained show that learning similarity measures yields significant improvements on several collections, for two prediction rules: the standard kNN rule, which was our primary goal, and a symmetric version of it.
A Computer Assisted Method for Nuclear Cataract Grading From Slit-Lamp Images Using Ranking

In clinical diagnosis, a grade indicating the severity of nuclear cataract is often manually assigned by a trained ophthalmologist to a patient after comparing the lens' opacity severity in his/her slit-lamp images with a set of standard photos. This grading scheme is often subjective and time-consuming. In this paper, a novel computer-aided diagnosis method via ranking is proposed to facilitate nuclear cataract grading following conventional clinical decision-making process. The grade of nuclear cataract in a slit-lamp image is predicted using its neighboring labeled images in a ranked image list, which is achieved using a learned ranking function. This ranking function is learned via direct optimization on a newly proposed approximation to a ranking evaluation measure. Our proposed method has been evaluated by a large dataset composed of 1000 different cases, which are collected from an ongoing clinical population-based study. Both experimental results and comparison with several existing methods demonstrate the benefit of grading via ranking by our proposed method.
Detection of unstained living neurospheres from phase contrast images with very large illumination variations

Live imaging of neural stem cells and progenitors is important to follow the biology of these cells. Non-invasive imaging techniques, such as phase contrast microscopy, are preferred as neural stem cells are very sensitive to photoxic damage cause by excitation of fluorescent molecules. However, large illumination variations and weak foreground/background contrast make phase contrast images challenging for image processing. In the current work, we propose a new method to segment neurospheres imaged under phase contrast microscopy by employing high dynamic range imaging and advanced level-set method. The use of high dynamic range imaging enhances the fused image by expressing cell signatures from various exposure captures. We apply advanced level-set method in cell segmentation to improve the detection rate over simple methods such as thresholding. Validation experiments in the analysis of 21 images containing over 400 cells have demonstrated accuracy improvements over existing techniques.
Towards Automatic Grading of Nuclear Cataract

Objective quantification of lens images is essential for cataract assessment and treatment. In this paper, bottom-up and top-down strategies are combined to detect the lens contour from the slit-lamp images. The center of the lens is localized by horizontal and vertical intensity profile clustering and the lens contour is estimated by fitting an ellipse. A modified active shape model (ASM) is further applied to detect the contour of the lens. The average intensity inside the lens is employed as the indicator of nuclear opacity. The relationship between our automated nuclear cataract assessment and the clinical grading is analyzed. The preliminary study of forty images shows that the difference between automatic grading and clinical grading is acceptable.
Automatic Area Classification in Peripheral Blood Smears

Cell enumeration and diagnosis using peripheral blood smears are routine tasks in many biological and pathological examinations. Not every area in the smear is appropriate for such tasks due to severe cell clumping or sparsity. Manual working-area selection is slow, subjective, inconsistent, and statistically biased. Automatic working-area classification can reproducibly identify appropriate working smear areas. However, very little research has been reported in the literature. With the aim of providing a preprocessing step for further detailed cell enumeration and diagnosis for high-throughput screening (HTS), we propose an integrated algorithm for area classification and quantify both cell spreading and cell clumping in terms of individual clumps and the occurrence probabilities of the group of clumps over the image. Comprehensive comparisons are presented to compare the effect of these quantifications and their combinations. Our experiments using images of Giemsa-stained blood smears show that the method is efficient, accurate (above 88.9% hit rates for all areas in the validation set of 140 images), and robust (above 78.1% hit rates for a test set of 4878 images). This lays a good foundation for fast working-area selection in HTS.
Fuzzy object patterns for visual indexing and segmentation

In this paper, we propose a fuzzy object pattern (FOP) for the representation of image content. The FOP is derived from view-based object recognition against a pre-defined vocabulary of visual object classes. Tessellation of FOPs over an image is further aggregated spatially to summarize the image content. This description scheme has been deployed in image indexing and retrieval on home photographs with very promising results. Furthermore, the FOP spans a new fuzzy pattern space in which incremental clustering is carried out to aggregate adjacent FOPs into larger regions. As a consequence, dominant regions can be segmented from an image.
Incremental Graph Clustering for Efficient Retrieval from Streaming Egocentric Video Data

With wearable devices like Google Glass, it will soon become possible to record everything we see. We envision a system where one's entire visual memory is captured, stored and indexed. One of the biggest challenges is the scale of the retrieval problem. In this work, we focus on how to organize streaming egocentric video data. Egocentric video data is highly redundant, in that, we see several objects and scenes repeatedly as we go about our lives. To exploit this redundancy, we propose an evolving sparse-graph representation for egocentric video data. We propose an incremental local density clustering scheme, which learns salient objects and scenes for streaming egocentric video data. We use the density clustering scheme to prune redundant data in the database. For image-retrieval applications, by retaining only representative nodes from dense sub graphs in the streaming data source, we show we can achieve 90% of peak recall by retaining only 1% of data, with a significant 18% improvement in absolute recall over naive uniform sub sampling of the egocentric video data.