Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem


We consider a variational convex relaxation of a class of optimal partitioning and multiclass labeling problems, which has recently proven quite successful and can be seen as a continuous analogue of Linear Programming (LP) relaxation methods for finite-dimensional problems. While for the latter several optimality bounds are known, to our knowledge no such bounds exist in the infinite-dimensional setting. We provide such a bound by analyzing a probabilistic rounding method, showing that it is possible to obtain an integral solution of the original partitioning problem from a solution of the relaxed problem with an a priori upper bound on the objective. The approach has a natural interpretation as an approximate, multiclass variant of the celebrated coarea formula.

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This publication is partly based on work supported by Award No. KUK-I1-007-43, made by King Abdullah University of Science and Technology (KAUST).

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Correspondence to Jan Lellmann.



Proof of Proposition 3

In order to prove the first assertion (88), note that the mapping wΨ(νw ) is convex, therefore it must assume its maximum on the polytope Δ l −Δ l :={z 1z 2|z 1,z 2∈Δ l } in a vertex of the polytope. Since the polytope Δ l −Δ l is the difference of two polytopes, its vertex set is at most the difference of their vertex sets, V:={e ie j|i,j∈{1,…,l}}. On this set, the bound Ψ(νw )⩽λ u holds for wV due to the upper-boundedness condition (25), which proves (88).

The second equality (90) follows from the fact that G:={b ik:=e k(e ie i+1)∣1⩽kd,1⩽il−1} is a basis of the linear subspace W, satisfying Ψ(b ik)⩽λ u , and Ψ is positively homogeneous and convex, and thus subadditive. Specifically, there is a linear transform T:W→ℝd×(l−1) such that w=∑ i,k b ik α ik for α=T(w). Then


Since (25) ensures Ψb ik)⩽λ u , we obtain

$$ \varPsi(w) \leqslant\lambda_u \sum_{i k} | \alpha_{i k} | \leqslant \lambda_u \|T\| \|w \|_2 $$

for any suitable operator norm ∥⋅∥ and any wW. □

Proof of Proposition 4

Denote \(\mathcal{B}_{\delta} :=\mathcal{B}_{\delta}(x)\). We prove mutual inclusion:

“⊆”: From the definition of the measure-theoretic interior,


Since \(|\mathcal{B}_{\delta} | \geqslant|\mathcal{B}_{\delta} \cap E| \geqslant|\mathcal{B}_{\delta} \cap E \cap F|\) (and vice versa for \(|\mathcal{B}_{\delta} \cap F|\)), it follows by the “sandwich” criterion that both \(\lim_{\delta\searrow0} |\mathcal{B}_{\delta} \cap E| / |\mathcal{B}_{\delta} |\) and \(\lim_{\delta\searrow0} |\mathcal{B}_{\delta} \cap F| / |\mathcal{B}_{\delta} |\) exist and are equal to 1, which shows xE 1F 1.

“⊇”: Assume that xE 1F 1. Then


We obtain equality,


from which we conclude that

$$ \lim_{\delta\searrow0} \sup\frac{|\mathcal{B}_{\delta} \cap E \cap F|}{|\mathcal{B}_{\delta} |} = \lim_{\delta\searrow0} \inf \frac{|\mathcal{B}_{\delta} \cap E \cap F|}{|\mathcal{B}_{\delta} |} = 1, $$

i.e., x∈(EF)1. □

Proof of Proposition 5

First note that


The inequality (∗) is a consequence of the definition of \(w^{\pm}_{\mathcal{F}E}\) and [2, Theorem 3.77], and (∗∗) follows directly from w(x),w(y)∈Δ l a.e. on Ω. The upper bound (187) permits applying [2, Theorem 3.84] on w, which provides \(w \in\operatorname{BV} (\varOmega)^{l}\) and (94). Due to [2, Proposition 3.61], the sets (E)0,(E)1 and \(\mathcal{F}E\) form a (pairwise disjoint) partition of Ω, up to an \(\mathcal{H}^{d - 1}\)-zero set. Therefore, since \(\varPsi(D u) \ll|D u| \ll\mathcal{H}^{d - 1}\) by construction, from [2, Theorem 2.37, 3.84] we obtain, for any Borel set A,


Since w(x)∈Δ l a.e. by assumption, we conclude that \(w^{+}_{\mathcal{F}E}\) and \(w^{-}_{\mathcal{F}E}\) must have values in Δ l as well, see [2, Theorem 3.77]. Therefore we can apply Proposition 3 to obtain


We rewrite Ψ(Dw) using (94),


From [2, Proposition 2.37] we obtain that Ψ is additive on mutually singular Radon measures μ,ν, i.e., if |μ|⊥|ν|, then


for any Borel set BΩ. This holds in particular for the three measures in (193), therefore


Since Du⌞(E)1≪|Du⌞(E)1|=|Du|⌞(E)1, we conclude Ψ(Dw)⌞(E)1=Ψ(Du)⌞(E)1 and Ψ(Dw)⌞(E)0=Ψ(Dv)⌞(E)0. Substitution into (192) proves the remaining assertion,



Proof of Proposition 6

We first show (98). It suffices to show that


This can be seen by considering the precise representative \(\widetilde{1_{E}}\) of 1 E [2, Definition 3.63]: Starting with the definition,


the fact that \(\lim_{\delta\searrow0} \frac{| \varOmega\cap\mathcal {B}_{\delta} (x) |}{|\mathcal{B}_{\delta} (x) |} = 1\) implies


Substituting E by ΩE, the same equivalence shows that \(x \in(E)^{0} \Leftrightarrow\widetilde{1_{\varOmega\setminus E}} (x) = 1 \Leftrightarrow\widetilde{1_{E}} (x) = 0\). As \(\mathcal{L}^{d} (\varOmega \setminus((E)^{0} \cup(E)^{1})) = 0\), this shows that \(1_{E^{1}} = \widetilde{1_{E}}\) \(\mathcal{L}^{d}\)-a.e. Using the fact that \(\widetilde {1_{E}} = 1_{E}\) [2, Proposition 3.64], we conclude that \(1_{(E)^{1}} = 1_{E}\) \(\mathcal{L}^{d}\)-a.e., which proves (197) and therefore the assertion (98).

Since the measure-theoretic interior (E)1 is defined over \(\mathcal{L}^{d}\)-integrals, it is invariant under \(\mathcal{L}^{d}\)-negligible modifications of E. Together with (197) this implies


To show the relation (Du)⌞(E)1=(Dv)⌞(E)1, consider


The equality (∗) holds due to the assumption (96), and due to the fact that Df=Dg if f=g \(\mathcal{L}^{d}\)-a.e. (see, e.g., [2, Proposition 3.2]). We continue from (204) via


Therefore Du⌞(E)1=Dv⌞(E)1. Then,


In the equality (∗) we used the additivity of Ψ on mutually singular Radon measures [2, Proposition 2.37]. By definition of the total variation, |μA|=|μ|⌞A holds for any measure μ, therefore |Du⌞(Ω∖(E)1)|=|Du|⌞(Ω∖(E)1) and |Du⌞(Ω∖(E)1)|((E)1)=0, which together with (again by definition) Ψ(μ)≪|μ| implies that the second term in (211) vanishes. Since all observations equally hold for v instead of u, we conclude


Equation (97) follows immediately. □

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Lellmann, J., Lenzen, F. & Schnörr, C. Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. J Math Imaging Vis 47, 239–257 (2013). https://doi.org/10.1007/s10851-012-0390-7

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  • Convex relaxation
  • Multiclass labeling
  • Approximation bound
  • Combinatorial optimization
  • Total variation
  • Linear programming relaxation