Shakhnarovich g learning task specific similarity phd thesis mit 2006

Shakhnarovich g learning task specific similarity phd thesis mit 2006

shakhnarovich g learning task specific similarity phd thesis mit Current PhD students: O mid Taheri, MPI for Intelligent Systems, Tübingen. Qianli Ma, Int. Max Planck Research School, Intelligent Systems, Tübingen. Vassilis Choutas, MPI-ETH Center for Shakhnarovich G Learning Task Specific Similarity Phd Thesis Mit , Autobiographical Essay, Best Essay Writing Service Sites, How Should A Essay Be Organized Shakhnarovich G Learning Task Specific Similarity Phd Thesis Mit & contrast essay and you think that few arguments are missing. Our writer will resolve the issue and will deliver again but without any reason, we do not Shakhnarovich G Learning Task Specific Similarity Phd Thesis Mit rewrite the whole essay second time for free

College essay: Shakhnarovich g learning task specific similarity phd thesis mit

The right measure of similarity between examples is important in many areas of computer science. In particular it is a critical component in example- based learning methods. Similarity is commonly defined in terms of a conventional distance function, but such a definition does not necessarily capture the inherent meaning of similarity, which tends to depend on the underlying task.

We develop an algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples.

Our learning algorithm constructs, in a greedy fashion, an encoding of the data. This encoding can be seen as an embedding into a space, where a weighted Hamming distance is correlated with the unknown similarity, shakhnarovich g learning task specific similarity phd thesis mit 2006.

This allows us to predict when two previously unseen examples are similar and, importantly, to efficiently search a very large database for examples similar to a query.

This approach is tested on a set of standard machine learning benchmark problems. The model of similarity learned with our algorithm provides and improvement over standard example-based classification and regression. We also apply this framework to problems in computer vision: articulated pose estimation of humans from single images, articulated tracking in video, and matching image regions subject to generic visual similarity.

This chapter defines some technical concepts, most importantly the notion of similarity we want to model, and provides a brief overview of the contributions of the thesis. Among the topics covered in this chapter: example-based classification and regression, previous work on learning distances and dis similarities such as MDSand algorithms for fast search and retrieval, with emphasis on locality sensitive hashing LSH.

This is the main algorithmic "meat" of the thesis: this chapter describes three algorithms for learning an embedding of the data into a weighted Hamming space, with the objective for L1 distance there to reflect the underlying similarity. The algorithms are: Similarity sensitive coding SSC. This algorithm discretizes each dimension of the data into zero or more bits. Each dimension is considered independently of the rest. Boosted SSC. A modification of SSC in which the code is constructed by greedily collecting discretization bits, thus removing the independence assumption.

This is an extension of the Boosted SSC, with each bit of the embedding obtained by thresholding a projection of the data not necessarily a single dimension. The underlying idea of all three algorithms is the same: build an embedding that, based on training examples of similar pairs, maps two similar objects close to each other with high probability. At the same time, there is an objective to control for "spread": the probability of arbitrary two objects in particular of dissimilar pairs of objects, if examples of such pairs are available to be close in the embedding space should be low.

This chapter also describes results of an evaluation of the proposed algorithms on seven benchmarks data sets from UCI and Delve repositories. An application of the ideas developed in previous chapters to the problem of pose estimation: inferring the articulated body pose e, shakhnarovich g learning task specific similarity phd thesis mit 2006.

the 3D positions of key joints, or values of joint angles from a single, monocular image containing a person. In a tracking scenario, a sequence of views, rather than a single view of a person, is available.

The motion provides additional cues, which are typically used in a probabilistic framework. In this chapter we show how similarity-based algorithms have been used to improve accuracy and speed of two articulated tracking systems: a general motion tracker and a motion-driven animation system focusing shakhnarovich g learning task specific similarity phd thesis mit 2006 swing dancing.

An important notion of similarity that is naturally conveyed by examples is the visual similarity of image regions. In this chapter we focus on a shakhnarovich g learning task specific similarity phd thesis mit 2006 definition of such similarity, namely invariance under rotation and slight shift.

We show how the machinery developed in Chapter 3 allows us to improve matching performance for two popular representations of image patches. Shimon UllmanWeizmann Institute of Science.

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Learning Task-Specific Similarity

Shakhnarovich G Learning Task Specific Similarity Phd Thesis Mit , Autobiographical Essay, Best Essay Writing Service Sites, How Should A Essay Be Organized Learning Task-Specific Similarity by Gregory Shakhnarovich Submitted to the Department of Electrical Engineering and Computer Science on September 30, , in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical shakhnarovich g learning task specific similarity phd thesis mit Current PhD students: O mid Taheri, MPI for Intelligent Systems, Tübingen. Qianli Ma, Int. Max Planck Research School, Intelligent Systems, Tübingen. Vassilis Choutas, MPI-ETH Center for