nils-herrmann/clean_scirepeval huggingface dataset:
Molecular classification of multiple tumor types

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.
Multiclass cancer diagnosis using tumor gene expression signatures

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.
Using the Fisher kernel method for Web audio classification

As the multimedia content of the Web increases techniques to automatically classify this content become more important. We present a system to classify audio files collected from the Web. The system classifies any audio file as belonging to one of three categories: speech, music and other. To classify the audio files, we use the technique of Fisher kernels. The technique as proposed by Jaakkola (1998) assumes a probabilistic generative model for the data, in our case a Gaussian mixture model. Then a discriminative classifier uses the GMM as an intermediate step to produce appropriate feature vectors. Support vector machines are our choice of discriminative classifier. We present classification results on a collection of more than 173 hours of Web audio randomly collected. We believe our results represent one of the first realistic studies of audio classification performance on found data. Our final system yielded a classification rate of 81.8%.
A mathematical theory of textons and primal sketch: integrating generative and descriptive methods

Textons, coined by Julesz in the 1960s, refer to the atomic structures in natural images and are considered the basic elements in early (pre-attentive) visual perception. The primal sketches, conjectured by Marr in the 1970s, refer to parsimonious token representation of generic images. Both concepts are fundamental to computer and human vision studies. However they have been very elusive due to the lack of rigorous mathematical definitions and models. The objective of this dissertation is to study a mathematical framework for the two concepts and thus to build a theoretical foundation for the early vision theory. Our study leads to generative image models for generic natural images and computing algorithms for learning, simulation, and inference. The framework integrates two main statistical modeling paradigms in the literature. (i) Hierarchic generative methods, such as transformed component analysis, wavelet coding, and sparse coding; and (ii) Descriptive methods, such as Markov random fields, graphical models, and minimax entropy learning. Generally speaking, the former decompose images into image elements, and the latter represent spatial relationships between the components. The dissertation makes the following contributions. (1) We define textons and image primitives as elements in the dictionaries of generative models which are learned from natural images through maximum likelihood estimation. (2) We study a Gestalt field for modeling the spatial relations among textons with reconfigurable neighborhoods. (3) We present a primal sketch model by dividing an image lattice into structural parts as textural parts which are modeled by the generative models and descriptive models respectively. (4) We study the information scaling phenomena and the transitions between various regimes of image representations, for example, the transition between textons and textures.
Bottom-up/top-down image parsing by attribute graph grammar

In this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living moms. We choose one class of primitives - 3D planar rectangles projected on images and six graph grammar production rules. Each production rule not only expands a node into its components, but also includes a number of equations that constrain the attributes of a parent node and those of its children. Thus our graph grammar is context sensitive. The grammar rules are used recursively to produce a large number of objects and patterns in images and thus the whole graph grammar is a type of generative model. The inference algorithm integrates bottom-up rectangle detection which activates top-down prediction using the grammar rules. The final results are validated in a Bayesian framework. The output of the inference is a hierarchical parsing graph with objects, surfaces, rectangles, and their spatial relations. In the inference, the acceptance of a grammar rule means recognition of an object, and actions are taken to pass the attributes between a node and its parent through the constraint equations associated with this production rule. When an attribute is passed from a child node to a parent node, it is called bottom-up, and the opposite is called top-down
A high resolution grammatical model for face representation and sketching

In this paper we present a generative, high resolution face representation which extends the well-known active appearance model (AAM) with two additional layers. (i) One layer refines the global AAM (PCA) model with a dictionary of learned face components to account for the shape and intensity variabilities of eyes, eyebrows, nose and mouth, (ii) the other layer divides the face skin into 9 zones with a learned dictionary of sketch primitives to represent skin marks and wrinkles. This model is no longer of fixed dimensions and is flexible for it can select the diverse representations in the dictionaries of face components and skin features depending on the complexity of the face. The selection is modulated by the grammatical rules through hidden "switch " variables. Our comparison experiments demonstrate that this model can achieve nearly lossless coding of face at high resolution (256 /spl times/ 256 pixels) with low bits. We also show that the generative model can easily generate cartoon sketches by changing the rendering dictionary. Our face model is aimed at a number of applications including cartoon sketch in non-photorealistic rendering, super-resolution in image processing, and low bit face communication in wireless platforms.
Computing three-dimensional scene from a single image by bottom-up/top-down bayesian inference

It is common experience for human vision to perceive full 3D shape and scene from a single 2D image with the occluded parts "filled-in" by prior visual knowledge. Thus, computing the 3D structures of all the objects in the scene from a single image is a fundamental problem in computer vision. In this thesis, we propose a bottom-up/top-down Bayesian inference framework to compute the 3D structures of objects in the scene from a single image, which integrates the involved visual tasks (segmentation, perceptual grouping, object detection and recognition, 3D reconstruction) in a principled way and incorporates the prior visual knowledge in the inference. 
The output of the inference framework is a hierarchical "parsing graph" with the scene label at the top (or root), objects with 3D structures and their parts at intermediate nodes, and image pixels at the bottom. The number of layers in this parsing graph is determined by the types of objects or visual patterns. The nodes in this parsing graph correspond to visual patterns represented by probabilistic models. The parsing graph also has both top-down connections and horizontal spatial connections, which correspond to the generative models and spatial relations modeled by Markov Random Field (MRF) respectively. 
Formulated in Bayesian framework, the inference algorithm computes the parsing graph from the input image by optimizing a posterior probability. In this optimization process, we integrate two popular computing paradigms in computer vision: generative methods, and discriminative methods. The former formulates the posterior probability to maximize in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative proposals using some bottom-up tests to drive the maximizing process in the solution space. Thus, the inference algorithm achieves both speed and consistency. 
We also investigate three mechanisms to efficiently construct the parsing graph based on the properties of visual patterns being computed: bottom-up construction mechanism, top-down construction mechanism, and bottom-up/top-down construction mechanism.
Cluster sampling and its applications to segmentation, stereo and motion

Many computer vision problems can be formulated as graph partition problems that minimize energy functions. Generally applicable algorithms like the Gibbs sampler can perform the minimization task, but they are very slow to converge, especially since the graphs in vision tasks are large (105–10 6 nodes). On the other hand, computationally effective algorithms like Graph Cuts and Belief Propagation are specialized to particular forms of energy functions, and they cannot be applied for complex statistical models using generative models and high-order priors. In this thesis, a new stochastic algorithm capable of sampling arbitrary energy functions defined on graph partitions is presented. To increase efficiency, the algorithm uses the image information to make informed jumps in the search space. The image information is given in the form of edge weights and represents an empirical probability that the nodes connected by the edge belong to the same object. At each step, the algorithm creates clusters of nodes by turning on/off the edges randomly according to their weights, and changes the label of all nodes in one cluster (connected component) in a single move. Each move is accepted or rejected according to an acceptance probability given by a simple and explicit equation. The algorithm is applied to 4 important problems in computer vision: image segmentation, perceptual organization, stereo matching and motion segmentation. To address different computational or representational issues, multi-grid, multi-level and multi-cue variants of the algorithm are presented. In image segmentation, the algorithm's performance is compared to the Gibbs sampler, while in stereo matching, it is compared to Graph Cuts and Belief Propagation.
Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities

Many vision tasks can be formulated as graph partition problems that minimize energy functions. For such problems, the Gibbs sampler provides a general solution but is very slow, while other methods, such as Ncut and graph cuts are computationally effective but only work for specific energy forms and are not generally applicable. In this paper, we present a new inference algorithm that generalizes the Swendsen-Wang method to arbitrary probabilities defined on graph partitions. We begin by computing graph edge weights, based on local image features. Then, the algorithm iterates two steps: (1) graph clustering - it forms connected components by cutting the edges probabilistically based on their weights; (2) graph relabeling - it selects one connected component and flips probabilistically, the coloring of all vertices in the component simultaneously. Thus, it realizes the split, merge, and regrouping of a "chunk" of the graph, in contrast to Gibbs sampler that flips a single vertex. We prove that this algorithm simulates ergodic and reversible Markov chain jumps in the space of graph partitions and is applicable to arbitrary posterior probabilities or energy functions defined on graphs. We demonstrate the algorithm on two typical problems in computer vision-image segmentation and stereo vision. Experimentally, we show that it is 100-400 times faster in CPU time than the classical Gibbs sampler and 20-40 times faster then the DDMCMC segmentation algorithm. For stereo, we compare performance with graph cuts and belief propagation. We also show that our algorithm can automatically infer generative models and obtain satisfactory results (better than the graphic cuts or belief propagation) in the same amount of time.
What are Textons?

Textons refer to fundamental micro-structures in natural images (and videos) and are considered as the atoms of pre-attentive human visual perception (Julesz, 1981). Unfortunately, the word “texton” remains a vague concept in the literature for lack of a good mathematical model. In this article, we first present a three-level generative image model for learning textons from texture images. In this model, an image is a superposition of a number of image bases selected from an over-complete dictionary including various Gabor and Laplacian of Gaussian functions at various locations, scales, and orientations. These image bases are, in turn, generated by a smaller number of texton elements, selected from a dictionary of textons. By analogy to the waveform-phoneme-word hierarchy in speech, the pixel-base-texton hierarchy presents an increasingly abstract visual description and leads to dimension reduction and variable decoupling. By fitting the generative model to observed images, we can learn the texton dictionary as parameters of the generative model. Then the paper proceeds to study the geometric, dynamic, and photometric structures of the texton representation by further extending the generative model to account for motion and illumination variations. (1) For the geometric structures, a texton consists of a number of image bases with deformable spatial configurations. The geometric structures are learned from static texture images. (2) For the dynamic structures, the motion of a texton is characterized by a Markov chain model in time which sometimes can switch geometric configurations during the movement. We call the moving textons as “motons”. The dynamic models are learned using the trajectories of the textons inferred from video sequence. (3) For photometric structures, a texton represents the set of images of a 3D surface element under varying illuminations and is called a “lighton” in this paper. We adopt an illumination-cone representation where a lighton is a texton triplet. For a given light source, a lighton image is generated as a linear sum of the three texton bases. We present a sequence of experiments for learning the geometric, dynamic, and photometric structures from images and videos, and we also present some comparison studies with K-mean clustering, sparse coding, independent component analysis, and transformed component analysis. We shall discuss how general textons can be learned from generic natural images.
Modeling complex motion: photometric, geometric, dynamic, and topological aspects

As natural scenes contain a huge number of motion patterns generated by various stochastic processes, how to represent and model these diverse visual patterns, and how to learn and compute the visual patterns efficiently are fundamental problems in computer vision. In this thesis, we present a unified theory with a generative statistical learning and inference framework to analyze complex visual patterns in both motion and scale-space. 
The goal of my research is to compute the semantic content of image sequences and representing them with symbolic graphs using generic visual vocabulary learned from natural images. Based on the generic graph representation, four important modeling issues are addressed, which are photometric, geometric, dynamic and topological issues. These four aspects span sub-dimensions in image space. The unified framework aims to answer questions like "What do we perceive when we look at a video sequence?" It tries to identify the perceptually meaningful basic elements and transitions in image sequences, model the elements' attributes and interactions in time-space and scale-space, and infer the hidden dynamic graph structures. We augment the model complexity progressively to learn various complicated visual patterns. We use three challenging examples to illustrate the learning and inference on these four aspects. Example (1): Textured motion modeling. Example (2): Topological changes in complex motion modeling. Example (3): Perceptual transitions in scale-space. The integrated model is generic. It can be broadly applied to many vision applications, such as tracking, video annotation, motion segmentation, document image analysis, surveillance, and automatic cartoon animation generation. The model learning and inference is achieved by employing Markov chain Monte Carlo (MCMC) sampling method.
Incorporating visual knowledge representation in stereo reconstruction

In this paper, we present a two-layer generative model that incorporates generic middle-level visual knowledge for dense stereo reconstruction. The visual knowledge is represented by a dictionary of surface primitives including various categories of boundary discontinuities and junctions in parametric form. Given a stereo pair, we first compute a primal sketch representation which decomposes the image into a structural part for object boundaries and high intensity contrast represented by a 2D sketch graph, and a structure less part represented by Markov random field on pixels. Then we label the sketch graph and compute the 3D sketch (like a wire-frame) by fitting the primitive dictionary to the sketch graph. The surfaces between the 3D sketches are filled in by computing the depth of the MRP model on the structureless part. These two levels interact closely since the MRF is used to propagate information between the primitives, and at the same time, the primitives act as boundary conditions for the MRF. The two processes maximize a Bayesian posterior probability jointly. We propose an MCMC algorithm that simultaneously infers the 3D primitive types and parameters and estimates the depth of the scene. Our experiments show that this representation can infer the depth map with sharp boundaries and junctions for textureless images, curve objects and free-form shapes.
Title Primal Sketch : Integrating Texture and Structure Permalink

Following Marr’s insight, we propose a generative image representation called primal sketch, which integrates two modeling components. The first component explains the structural part of an image, such as object boundaries, by a hidden layer of image primitives. The second component models the remaining textural part without distinguishable elements by Markov random fields that interpolate the structural part of the image. We adopt an artist’s notion by calling the two components “sketchable” and “non-sketchable” parts respectively. A dictionary of image primitives are used for modeling structures in natural images, and each primitive is specified by variables for its photometric, geometric, and topological attributes. The primitives in the image representation are not independent but organized in an sketch graph. This sketch graph is modeled by a spatial Markov model that enforces Gestalt organizations. The inference of the sketch graph consists of two phases. Phase I sequentially adds the most prominent image primitives in a procedure similar to matching pursuit. Phase II edits the sketch graph by a number of graph operators to achieve good Gestalt organizations. Experiments show that the primal sketch model produces satisfactory results for a large number of generic images. The primal sketch model is not only a parsimonious image representation for lossy image coding, but also provides a meaningful mid-level generic representation for other vision tasks. Preprint of the Department of Statistics, UCLA, 2005. Some materials in this paper have been published in the Proceedings of the Int’l Conf. on Computer Vision, 2003, and the Workshop on Generative Model Based Vision, 2004, organized by Arthur Pece. We thank Dr. Pece for his editorial help.
Perceptual Scale Space and its Applications

In this paper, we study a perceptual scale space by constructing a so-called sketch pyramid which augments the Gaussian and Laplacian pyramid representations in traditional image scale space theory. Each level of this sketch pyramid is a generic attributed graph - called the primal sketch which is inferred from the corresponding image at the same level of the Gaussian pyramid. When images are viewed at increasing resolutions, more details are revealed. This corresponds to perceptual transitions which are represented by topological changes in the sketch graph in terms of a graph grammar. We compute the sketch or perceptual pyramid by Bayesian inference upwards-downwards the pyramid using Markov chain Monte Carlo reversible jumps. We show two example applications of this perceptual scale space: (1) motion tracking of objects over scales, and (2) adaptive image displays which can efficiently show a large high resolution image in a small screen (of a PDA for example) through a selective tour of its image pyramid. Other potential applications include super resolution and multiresolution object recognition.