A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods

High fidelity compression of irregularly sampled height fields

This paper presents a method to compress irregularly sampled height-fields based on a multi-resolution framework. Unlike many other height-field compression techniques, no resampling is required so the original height-field data is recovered (less quantization error). The method decomposes the compression task into two complementary phases: an in-plane compression scheme for (x, y) coordinate positions, and a separate multi-resolution z compression step. This decoupling allows subsequent improvements in either phase to be seamlessly integrated and also allows for independent control of bit-rates in the decoupled dimensions, should this be desired. Results are presented for a number of height-field sample sets quantized to 12 bits for each of x and y, and 10 bits for z. Total lossless encoded data sizes range from 11 to 24 bits per point, with z bit-rates lying in the range 2.9 to 8.1 bits per z coordinate. Lossy z bit-rates (we do not lossily encode x and y) lie in the range 0.7 to 5.9 bits per z coordinate, with a worst-case root-mean-squared (RMS) error of less than 1.7% of the z range. Even with aggressive lossy encoding, at least 40% of the point samples are perfectly reconstructed

On Optimal Quantization and its Effect on Anomaly Detection and Image Classification

This thesis presents the use of density estimation for performing data classification in different applications such as stream processing as well as image classification. The first half of this thesis presents a system that can process and analyze streaming data and extract the time frames that contain potential events of interest or anomalies without requiring any prior domain knowledge. The proposed method performs real time monitoring and mining of streaming data at multiple temporal scales simultaneously to maximize the probability of detection of anomalous events that span different lengths of time. The method does not assume the data segments containing anomalies belong to any particular distribution and therefore does not require prior domain knowledge. The system learns the evolution of normal behavior in streaming data and builds a model over time and uses it to determine whether the new incoming data fits that model. When analyzing streaming data, it is important for the algorithm to be fast with low computational complexity and therefore such aspects as well as the detection accuracy are studied and the results are presented. The algorithm is general and can be used for any type of streaming data. In the second half of this thesis, the feasibility of using density estimation in higher dimensions and in particular for visual descriptors is presented. A method for classifying images is proposed which uses density estimation to optimally quantize the feature space to generate a codebook used by a bag-of-features (BoF) image classifier. This thesis shows that the optimal smoothing calculation in density estimation can be used to systematically quantize the feature space to generate codebooks that can be used in image classification

Designing an orientation finding algorithm using human visual data

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1993.Includes bibliographical references (leaves 136-140).by Mojgan Monika Gorkani.M.S

Automatic Foreground Initialization for Binary Image Segmentation

Foreground segmentation is a fundamental problem in computer vision. A popular approach for foreground extraction is through graph cuts in energy minimization framework. Most existing graph cuts based image segmentation algorithms rely on user’s initialization. In this work, we aim to find an automatic initialization for graph cuts. Unlike many previous methods, no additional training dataset is needed. Collecting a training set is not only expensive and time consuming, but it also may bias the algorithm to the particular data distribution of the collected dataset. We assume that the foreground differs significantly from the background in some unknown feature space and try to find the rectangle that is most different from the rest of the image by measuring histograms dissimilarity. We extract multiple features, design a ranking function to select good features, and compute histograms based on integral images. The standard graph cuts binary segmentation is applied, based on the color models learned from the initial rectangular segmentation. Then the steps of refining the color models and re-segmenting the image iterate in the grabcut manner, until convergence, which is guaranteed. The foreground detection algorithm performs well and the segmentation is further improved by graph cuts. We evaluate our method on three datasets with manually labelled foreground regions, and show that we reach the similar level of accuracy compared to previous work. Our approach, however, has an advantage over the previous work that we do not require a training dataset

Analyzing the Fine Structure of Distributions

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to non-experts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.Comment: 66 pages, 81 figures, accepted in PLOS ON

Study of random process theory Final report, 1 Jul. 1965 - 1 Apr. 1966

Random process theory applied to discrete stationary and nonstationary data processing techniques - autocorrelation, and optimum smoothing for stationary processe