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«THE UNIVERSITY OF CHICAGO GRAMMATICAL METHODS IN COMPUTER VISION A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE PHYSICAL SCIENCES IN ...»

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Definition 6.2.2. A decomposition family F = (I, D) is θ-flexible if, for every interval [i, j) ∈ I, and every k such that i k j, there is a decomposition

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A θ-flexible decomposition family is desirable because, for any binary parse tree, there is a similar parse tree that is allowable under the decomposition family. Here, similar means that, as we recursively subdivide a curve into subcurves, we can always choose a midpoint whose index differs from a given midpoint by at most a fixed percentage (e.g., 10% when θ = 10 or 5% when θ = 20 ) of the length of the subcurve, as shown in Figure 6.1. A θ-flexible decomposition family thus approximates the complete decomposition family, in some sense.

Another notion of approximation is θ-completeness:

Definition 6.2.3. Let s be a string of length n. Let F = (I, D) be a decomposition family for s. An interval [i, j) is called reachable if there is a sequence of intervals xt such that x0 = [0, n), xk = [i, j), and there is a decomposition with xt on the left-hand side and xt+1 on the right-hand side for every t between 0 and k − 1.

Definition 6.2.4. A decomposition family F = (I, D) is θ-complete if, for every interval [i, j), 0 ≤ i j ≤ n, there is a reachable interval [i, j ) ∈ I such that

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Proof. Let [i, j) be an interval. Starting with the interval [0, n), we build a sequence of intervals by picking midpoints so that [i, j) is wholly contained in one side or the other each time. At each step, we then take the interval containing [i, j) as the next interval in our sequence.

When we can no longer do this, we have an interval [k, ) such that k ≤ i j ≤. We claim that k − i 2θ(j − i)

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Decomposition families are interesting because they allow us to speed up parsing by searching over a restricted set of parses.

Theorem 6.3.

1. Let G be a context-free grammar with k rules. Let s be a string of length n. Let F = (I, D) be a decomposition family for s. Then there is a dynamic programming algorithm (Algorithm 8) to approximately (relative to F) parse s in time O(|D|k).

Traditional parsing corresponds to the complete decomposition family, which has size Θ(n3 ). Algorithm 8 with the complete decomposition family is equivalent to Algorithm 7.

Fortunately, we can construct sparse decomposition families of size O(n), which leads to a linear time approximate parsing algorithm.

Algorithm 8 Parsing with a decomposition family Input: string s, decomposition family (I, D), context-free grammar (X, R) for X ∈ X do for i = 0 to n − 1 do COST [X, [i, i + 1)] ← w(X → s[i]) end for end for for [i, j) ∈ I, ordered by increasing length do COST [X, [i, j)] ← ∞ for k such that [i, j) → [i, k)[k, j) ∈ D do for X → Y Z ∈ R do COST [X, [i, j)] ← min{COST [X, [i, j)], w(X → Y Z) + COST [Y, [i, k)] + COST [Z, [k, j)]} end for end for end for Figure 6.2: Illustrating the construction from Theorem 6.4.1, with k = 4. Rectangles denote portions of the string between members of the index set. A selection of intervals that live at each level are shown.

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Theorem 6.4.

1. Let s be a string of length n, and let k be an integer. Then there is a 1 decomposition family (I, D) for s that has at most 2nk 2 decompositions.

k -flexible Proof. Let C be the set of integers from 0 to n, inclusive. We will call this the index set.

We recursively define a sequence of subsampled index sets C (i) as follows: C (0) = C, and C (i+1) is obtained from C (i) by taking every other index. The number of indices in all C (i) combined is at most 2n.

We will talk about the length of an interval relative to an index set. An interval [a, b) in index set C (i) if there are − 1 indices between a and b in C (i).

has length An interval [a, b) is allowable if there is some C (i) which contains both a and b, and [a, b) has length at most k in C (i), i.e., there are at most k − 1 other indices between them in C (i).

The number of allowable intervals is then at most 2nk.

We will say that an interval [a, b) lives in level i (for i ≥ 0) if a and b are both in C (i), and if the length of the interval relative to C (i) is strictly greater than k/2 and at most k.

When i = 0, we will not have the minimum length requirement, so all intervals of C of length at most k live in level 0. It is straightforward to see that each allowable interval lives in a single level.

If [a, b) is an interval that lives in level i, then we can decompose it as [a, b) → [a, c)[c, b) by picking midpoints c from among the indices in C (i). Some of these choices will lead to intervals that live in level i − 1.

This decomposition family is k -flexible. When picking midpoints at level i, our interval has length at least k/2 in C (i), so we have at least k/2 evenly spaced midpoint choices. We can thus choose a midpoint that is within c points of any desired midpoint, where c is at most k times the length of the interval we are splitting.

There are at most k midpoints per allowable interval, so the number of composition rules is at most 2nk 2.

This demonstrates the existence of linear-size θ-flexible decomposition families, which yields a linear time approximate parsing algorithm. This in turn means that we can use context-free grammars to parse long strings, which expands the range of domains where we can use context-free grammar models.





APPENDIX A

MODELS OF LEAVES

Figure A.1: Training examples from class #1.

Figure A.2: Samples from learned model for class #1.

Figure A.3: Training examples from class #2.

Figure A.4: Samples from learned model for class #2.

Figure A.5: Training examples from class #3.

Figure A.6: Samples from learned model for class #3.

Figure A.7: Training examples from class #4.

Figure A.8: Samples from learned model for class #4.

Figure A.9: Training examples from class #5.

Figure A.10: Samples from learned model for class #5.

Figure A.11: Training examples from class #6.

Figure A.12: Samples from learned model for class #6.

Figure A.13: Training examples from class #7.

Figure A.14: Samples from learned model for class #7.

Figure A.15: Training examples from class #8.

Figure A.16: Samples from learned model for class #8.

Figure A.17: Training examples from class #9.

Figure A.18: Samples from learned model for class #9.

Figure A.19: Training examples from class #10.

Figure A.20: Samples from learned model for class #10.

Figure A.21: Training examples from class #11.

Figure A.22: Samples from learned model for class #11.

Figure A.23: Training examples from class #12.

Figure A.24: Samples from learned model for class #12.

Figure A.25: Training examples from class #13.

Figure A.26: Samples from learned model for class #13.

Figure A.27: Training examples from class #14.

Figure A.28: Samples from learned model for class #14.

Figure A.29: Training examples from class #15.

Figure A.30: Samples from learned model for class #15.

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