Browsing by Author "Cherian, Anoop"

Now showing 1 - 4 of 4
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    Autonomous altitude estimation of a miniature helicopter using a single onboard camera.
    (2010-04) Cherian, Anoop
    Autonomous estimation of the altitude of an Unmanned Aerial Vehicle (UAV) is extremely important when dealing with flight maneuvers like landing, steady flight, etc. Vision based techniques for solving this problem have been underutilized. In this thesis, we propose a new algorithm to estimate the altitude of a UAV from top-down aerial images taken from a single on-board camera. We use a semi-supervised machine learning approach to solve the problem. The basic idea of our technique is to learn the mapping between the texture information contained in an image to a possible altitude value. We learn an over complete sparse basis set from a corpus of unlabeled images capturing the texture variations. This is followed by regression of this basis set against a training set of altitudes. Finally, a spatio-temporal Markov Random Field is modeled over the altitudes in test images, which is maximized over the posterior distribution using the MAP estimate by solving a quadratic optimization problem with L1 regularity constraints. The method is evaluated in a laboratory setting with a real helicopter and is found to provide promising results with sufficiently fast turnaround time.
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    Efficient Similarity Search via Sparse Coding
    (2011-11-21) Cherian, Anoop; Morellas, Vassilios
    This work presents a new indexing method using sparse coding for fast approximate Nearest Neighbors (NN) on high dimensional image data. To begin with we sparse code the data using a learned basis dictionary and an index of the dictionary's support set is next used to generate one compact identifier for each data point. As basis combinations increase exponentially with an increasing support set, each data point is likely to get a unique identifier that can be used to index a hash table for fast NN operations. When dealing with real world data, the identifiers corresponding to the query point and the true nearest neighbors in the database seldom match exactly (due to image noise, distortion, etc.). To accommodate these near matches, we propose a novel extension of the framework that utilizes the regularization path of the LASSO formulation to create robust hash codes. Experiments are conducted on large datasets and demonstrate that our algorithm rivals state-of-the-art NN techniques in search time, accuracy and memory usage.
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    Jensen-Bregman LogDet Divergence for Efficient Similarity Computations on Positive Definite Tensors
    (2012-05-02) Cherian, Anoop; Sra, Suvrit; Banerjee, Arindam
    Covariance matrices provide an easy platform for fusing multiple features compactly and as a result have found immense success in several computer vision applications, including activity recognition, visual surveillance, and diffusion tensor imaging. An important task in all of these applications is to compute the distance between covariance matrices using a (dis)similarity function, for which the natural choice is the Riemannian metric corresponding to the manifold inhabited by these matrices. As this Riemannian manifold is not flat, the dissimilarities should take into account the curvature of the manifold. As a result such distance computations tend to slow down, especially when the matrix dimensions are large or gradients are required. Further, suitability of the metric to enable efficient nearest neighbor retrieval is an important requirement in the contemporary times of big data analytics. To alleviate these difficulties, this paper proposes a novel dissimilarity measure for covariances, the Jensen-Bregman LogDet Divergence (JBLD). This divergence enjoys several desirable theoretical properties, at the same time is computationally less demanding (compared to standard measures). To address the problem of efficient nearest neighbor retrieval on large covariance datasets, we propose a metric tree framework using kmeans clustering on JBLD. We demonstrate the superior performance of JBLD on covariance datasets from several computer vision applications.
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    Similarity search in visual data
    (2013-01) Cherian, Anoop
    Contemporary times have witnessed a significant increase in the amount of data available on the Internet. Organizing such big data so that it is easily and promptly accessible, is a necessity that has been growing in importance. Among the various data modalities such as text, audio, etc., visual data (in the form of images and videos) constitute a major share of this available content. Contrary to other data modalities, visual data pose several significant challenges to storage and retrieval, namely (i) choosing an appropriate representation that can capture the essence of visual data is often non-trivial, and (ii) visual search and retrieval are often subjective, as a result computing semantically meaningful results is hard. On the other hand, visual data possesses rich structure. Exploiting this structure might help address these challenges. Motivated by these observations, this thesis explores new algorithms for efficient similarity search in structured visual data; “structure” is synonymous with the mathematical representation that captures desirable data properties. We will deal with two classes of such structures that are common in computer vision, namely (i) symmetric positive definite matrices as covariances, and (ii) sparse data representations in a dictionary learned from the data. Covariance valued data has found immense success in several mainstream computer vision applications such as visual surveillance, emotion recognition, face recognition, etc. Moreover, it is of fundamental importance in several other disciplines such as magnetic resonance imaging, speech recognition, etc. A technical challenge in computing similarities on such matrix valued data is their non-Euclidean nature. These matrices belong to a curved manifold where distances between data points are no more along straight lines, but along curved geodesics. As a result, state-of-the-art measures for comparing covariances tend to be slow. To address this issue, we propose a novel similarity measure on covariance matrices-the Jensen-Bregman LogDet divergence-which is fast, but at the same time preserves the accuracy of retrieval compared to natural distances on the manifold. To scale our retrieval framework for large covariance datasets, we propose a metric tree data structure on this new measure. Next, as clustering forms an important ingredient for several search algorithms, we investigate this component independently and propose a novel unsupervised algorithm based on the Dirichlet process mixture model for clustering covariance valued data. The second part of this thesis addresses similarity search problems for high dimensional vector valued data. Such data is ubiquitous not only in computer vision, but also in several other disciplines including data mining, machine learning, and robotics. As the dimensionality of the data increases, computing meaningful similarities becomes increasingly difficult due to the curse of dimensionality. Our approach to deal with this problem is inspired from the principles of dictionary learning and sparse coding. Our main idea is to learn an overcomplete dictionary of subspaces from the data so that each data point can be approximated by a sparse linear combination of these subspaces. We introduce a tuple based data descriptor on these sparse combinations-Subspace Combination Tuple-that is storage efficient, fast in retrieval, and provides superior accuracy for NN retrieval against the state-of-the-art. These benefits come at a price; the sparse representations are often sensitive to data perturbations. To circumvent this issue, we propose several algorithms for robust dictionary learning and sparse coding. Extending the sparse coding framework to matrix valued data for hashing covariances forms the content for the third part of this thesis. Towards this end, we propose our novel Generalized dictionary learning framework. We describe the theoretical motivations and provide extensive experimental evidence for demonstrating the benefits of our algorithms.