Complexity of Representation and Inference in Compositional Models with
    Part Sharing
Authors: Alan Yuille, Roozbeh Mottaghi
ICLR 2013

Abstract: This paper describes serial and parallel compositional models of multiple objects with part sharing. Objects are built by part-subpart compositions and expressed in terms of a hierarchical dictionary of object parts. These parts are represented on lattices of decreasing sizes which yield an executive summary description. We describe inference and learning algorithms for these models. We analyze the complexity of this model in terms of computation time (for serial computers) and numbers of nodes (e.g., 'neurons') for parallel computers. In particular, we compute the complexity gains by part sharing and its dependence on how the dictionary scales with the level of the hierarchy. We explore three regimes of scaling behavior where the dictionary size (i) increases exponentially with the level, (ii) is determined by an unsupervised compositional learning algorithm applied to real data, (iii) decreases exponentially with scale. This analysis shows that in some regimes the use of shared parts enables algorithms which can perform inference in time linear in the number of levels for an exponential number of objects. In other regimes part sharing has little advantage for serial computers but can give linear processing on parallel computers.

Complexity of Representation and Inference in
Compositional Models with Part Sharing
Alan L. Yuille
Depts. of Statistics, Computer Science & Psychology
University of California, Los Angeles
yuille@stat.ucla.edu
Roozbeh Mottaghi
Department of Computer Science
University of California, Los Angeles
roozbehm@cs.ucla.edu
Abstract
This paper describes serial and parallel compositional models of multiple objects
with part sharing. Objects are built by part-subpart compositions and expressed
in terms of a hierarchical dictionary of object parts. These parts are represented
on lattices of decreasing sizes which yield an executive summary description. We
describe inference and learning algorithms for these models. We analyze the com-
plexity of this model in terms of computation time (for serial computers) and num-
bers of nodes (e.g., ”neurons”) for parallel computers. In particular, we compute
the complexity gains by part sharing and its dependence on how the dictionary
scales with the level of the hierarchy. We explore three regimes of scaling be-
havior where the dictionary size (i) increases exponentially with the level, (ii) is
determined by an unsupervised compositional learning algorithm applied to real
data, (iii) decreases exponentially with scale. This analysis shows that in some
regimes the use of shared parts enables algorithms which can perform inference
in time linear in the number of levels for an exponential number of objects. In
other regimes part sharing has little advantage for serial computers but can give
linear processing on parallel computers.
1 Introduction
A fundamental problem of vision is how to deal with the enormous complexity of images and visual
scenes 1. The total number of possible images is almost inﬁnitely large [8]. The number of objects
is also huge and has been estimated at around 30,000 [2]. How can a biological, or artiﬁcial, vision
system deal with this complexity? For example, considering the enormous input space of images
and output space of objects, how can humans interpret images in less than 150 msec [16]?
There are three main issues involved. Firstly, how can a visual system be designed so that it can
efﬁciently represent large classes of objects, including their parts and subparts? Secondly, how can
the visual system be designed so that it can rapidly infer which object, or objects, are present in an
input image and the positions of their subparts? And, thirdly, how can this representation be learnt
in an unsupervised, or weakly supervised fashion? In short, what visual architectures enable us to
address these three issues?
Many considerations suggest that visual architectures should be hierarchical. The structure of mam-
malian visual systems is hierarchical with the lower levels (e.g., in areas V1 and V2) tuned to small
image features while the higher levels (i.e. in area IT) are tuned to objects 2. Moreover, as ap-
preciated by pioneers such as Fukushima [5], hierarchical architectures lend themselves naturally to
1Similar complexity issues will arise for other perceptual and cognitive modalities.
2But just because mammalian visual systems are hierarchical does not necessarily imply that this is the best
design for computer vision systems.
arXiv:1301.3560v1  [cs.CV]  16 Jan 2013
efﬁcient representations of objects in terms of parts and subparts which can be shared between many
objects. Hierarchical architectures also lead to efﬁcient learning algorithms as illustrated by deep
belief learning and others [7]. There are many varieties of hierarchical models which differ in details
of their representations and their learning and inference algorithms [14, 7, 15, 1, 13, 18, 10, 3, 9].
But, to the best of our knowledge, there has been no detailed study of their complexity properties.
This paper provides a mathematical analysis of compositional models [6], which are a subclass of the
hierarchical models. The key idea of compositionality is to explicitly represent objects by recursive
composition from parts and subparts. This gives rise to natural learning and inference algorithms
which proceed from sub-parts to parts to objects (e.g., inference is efﬁcient because a leg detector
can be used for detecting the legs of cows, horses, and yaks). The explicitness of the object rep-
resentations helps quantify the efﬁciency of part-sharing and make mathematical analysis possible.
The compositional models we study are based on the work of L. Zhu and his collaborators [19, 20]
but we make several technical modiﬁcations including a parallel re-formulation of the models. We
note that in previous papers [19, 20] the representations of the compositional models were learnt in
an unsupervised manner, which relates to the memorization algorithms of Valiant [17]. This paper
does not address learning but instead explores the consequence of the representations which were
learnt.
Our analysis assumes that objects are represented by hierarchical graphical probability models 3
which are composed from more elementary models by part-subpart compositions. An object – a
graphical model with Hlevels – is deﬁned as a composition of rparts which are graphical models
with H−1 levels. These parts are deﬁned recursively in terms of subparts which are represented by
graphical models of increasingly lower levels. It is convenient to specify these compositional models
in terms of a set of dictionaries {Mh : h= 1,..,, H}where the level-hparts in dictionary Mh are
composed in terms of level- h−1 parts in dictionary Mh−1. The highest level dictionaries MH
represent the set of all objects. The lowest level dictionaries M1 represent the elementary features
that can be measured from the input image. Part-subpart composition enables us to construct a
very large number of objects by different compositions of elements from the lowest-level dictionary.
It enables us to perform part-sharing during learning and inference, which can lead to enormous
reductions in complexity, as our mathematical analysis will show.
There are three factors which enable computational efﬁciency. The ﬁrst ispart-sharing, as described
above, which means that we only need to perform inference on the dictionary elements. The second
is the executive-summary principle. This principle allows us to represent the state of a part coarsely
because we are also representing the state of its subparts (e.g., an executive will only want to know
that ”there is a horse in the ﬁeld” and will not care about the precise positions of its legs). For
example, consider a letter T which is composed of a horizontal and vertical bar. If the positions
of these two bars are speciﬁed precisely, then we can specify the position of the letter T more
crudely (sufﬁcient for it to be ”bound” to the two bars). This relates to Lee and Mumford’s high-
resolution buffer hypothesis [11] and possibly to the experimental ﬁnding that neurons higher up
the visual pathway are tuned to increasingly complex image features but are decreasingly sensitive
to spatial position. The third factor is parallelism which arises because the part dictionaries can
be implemented in parallel, essentially having a set of receptive ﬁelds, for each dictionary element.
This enables extremely rapid inference at the cost of a larger, but parallel, graphical model.
The compositional section (2) introduces the key ideas. Section (3) describes the inference algo-
rithms for serial and parallel implementations. Section (4) performs a complexity analysis and
shows potential exponential gains by using compositional models.
2 The Compositional Models
Compositional models are based on the idea that objects are built by compositions of parts which,
in turn, are compositions of more elementary parts. These are built by part-subpart compositions.
3These graphical models contain closed loops but with restricted maximal clique size.
2
(a)
 (b)
Figure 1: (a) Compositional part-subpart models for T and L are constructed from the same ele-
mentary components τ1,τ2, horizontal and vertical bar using different spatial relations λ = (µ,σ),
which impose locality. The state xof the parent node gives the summary position of the object, the
executive summary, while the positions x1,x2 of the components give details about its components.
(b) The hierarchical lattices. The size of the lattices decrease with scale by a factorqwhich helps en-
force executive summary and prevent having multiple hypotheses which overlap too much.q= 1/4
in this ﬁgure.
2.1 Compositional Part-Subparts
We formulate part-subpart compositions by probabilistic graphical model which speciﬁes how a part
is composed of its subparts. A parent nodeνof the graph represents the part by its typeτν and a state
variable xν (e.g., xν could indicate the position of the part). The rchild nodes Ch(ν) = (ν1,...,ν r)
represent the parts by their types τν1 ,...,τ νr and state variables ⃗ xCh(ν) = (xν1 ,...,x νr) 4. The type
of the parent node is speciﬁed byτν = (τν1 ,...,τ νr,λν). Here (τν1 ,...,τ νr) are the types of the child
nodes, and λν speciﬁes a distribution over the states of the subparts (e.g.,over their relative spatial
positions). Hence the type τν of the parent speciﬁes the part-subpart compositional model.
The probability distribution for the part-subpart model relates the states of the part and the subparts
by:
P(⃗ xCh(ν)|xν; τν) =δ(xν −f(⃗ xCh(ν)))h(⃗ xCh(ν); λν). (1)
Here f(.) is a deterministic function, so the state of the parent node is determined uniquely by
the state of the child nodes. The function h(.) speciﬁes a distribution on the relative states of
the child nodes. The distribution P(⃗ xCh(ν)|xν; τν) obeys a locality principle, which means that
P(⃗ xCh(ν)|xν; τν) = 0, unless |xνi −xν|is smaller than a threshold for all i= 1,..,r . This require-
ment captures the intuition that subparts of a part are typically close together.
The state variable xν of the parent node provides an executive summary description of the part.
Hence they are restricted to take a smaller set of values than the state variables xνi of the subparts.
Intuitively, the state xν of the parent offers summary information (e.g., there is a cow in the right
side of a ﬁeld) while the child states ⃗ xCh(ν) offer more detailed information (e.g., the position of
the parts of the cow). In general, information about the object is represented in a distributed manner
with coarse information at the upper levels of the hierarchy and more precise information at lower
levels.
We give examples of part-subpart compositions in ﬁgure (1(a)). The compositions represent the
letters T and L, which are the types of the parent nodes. The types of the child nodes are horizontal
and vertical bars, indicated by τ1 = H, τ2 = V. The child state variables x1,x2 indicate the image
positions of the horizontal and vertical bars. The state variablexof the parent node gives a summary
description of the position of the lettersT and L. The compositional models for lettersT and Ldiffer
by their λparameter which species the relative positions of the horizontal and vertical bars. In this
example, we choose h(.; λ) to a Gaussian distribution, so λ = (µ,σ) where µis the mean relative
positions between the bars and σ is the covariance. We set f(x1,x2) = (1/2)(x1 + x2), so the
state of the parent node speciﬁes the average positions of the child nodes (i.e. the positions of the
two bars). Hence the two compositional models for the T and Lhave types τT = (H,V,λ T) and
τL = (H,V,λ L).
4In this paper, we assume a ﬁxed value rfor all part-subpart compositions.
3
OR
Figure 2: Left Panel: Two part-subpart models. Center Panel: Combining two part-subpart models
by composition to make a higher level model. Right Panel: Some examples of the shapes that can
be generated by different parameters settings λof the distribution.
2.2 Models of Object Categories
An object category can be modeled by repeated part-subpart compositions. This is illustrated in
ﬁgure (2) where we combine T’s and L’s with other parts to form more complex objects. More
generally, we can combine part-subpart compositions into bigger structures by treating the parts as
subparts of higher order parts.
More formally, an object category of typeτHis represented by a probability distribution deﬁned over
a graph V. This graph has a hierarchical structure with levels h∈{0,..., H}, where V= ⋃H
h=0 Vh.
Each object has a single, root node, at level- H(i.e. VHcontains a single node). Any node ν ∈Vh
(for h >0) has r children nodes Ch(ν) in Vh−1 indexed by (ν1,...,ν r). Hence there are rH−h
nodes at level-h (i.e. |Vh|= rH−h).
At each node νthere is a state variable xν which indicates spatial position and type τν. The type τH
of the root node indicates the object category and also speciﬁes the types of its parts.
The position variables xν take values in a set of lattices {Dh : h= 0,..., H}, so that a level-h node,
ν ∈Vh, takes position xν ∈Dh. The leaf nodes V0 of the graph take values on the image latticeD0.
The lattices are evenly spaced and the number of lattice points decreases by a factor of q <1 for
each level, so |Dh|= qh|D0|, see ﬁgure (1(b)). This decrease in number of lattice points imposes
the executive summary principle. The lattice spacing is designed so that parts do not overlap. At
higher levels of the hierarchy the parts cover larger regions of the image and so the lattice spacing
must be larger, and hence the number of lattice points smaller, to prevent overlapping5.
The probability model for an object category of type τH is speciﬁed by products of part-subpart
relations:
P(⃗ x|τH) =
∏
ν∈V/V0
P(⃗ xCh(ν)|xν; τν)U(xH). (2)
Here U(xH) is the uniform distribution.
2.3 Multiple Object Categories, Shared Parts, and Hierarchical Dictionaries
Now suppose we have a set of object categories τH ∈H, each of which can be expressed by an
equation such as equation (2). We assume that these objects share parts. To quantify the amount of
part sharing we deﬁne a hierarchical dictionary {Mh : h = 0,..., H}, where Mh is the dictionary
of parts at level h. This gives an exhaustive set of the parts of this set of the objects, at all levels
h = 0,..., H. The elements of the dictionary Mh are composed from elements of the dictionary
Mh−1 by part-subpart compositions 6.
This gives an alternative way to think of object models. The type variable τν of a node at level h
(i.e. in Vh) indexes an element of the dictionary Mh. Hence objects can be encoded in terms of
the hierarchical dictionary. Moreover, we can create new objects by making new compositions from
existing elements of the dictionaries.
5Previous work [20, 19] was not formulated on lattices and used non-maximal suppression to achieve the
same effect.
6The unsupervised learning algorithm in [19] automatically generates this hierarchical dictionary.
4
2.4 The Likelihood Function and the Generative Model
To specify a generative model for each object category we proceed as follows. The prior speciﬁes a
distribution over the positions and types of the leaf nodes of the object model. Then the likelihood
function is speciﬁed in terms of the type at the leaf nodes (e.g., if the leaf node is a vertical bar, then
there is a high probability that the image has a vertical edge at that position).
More formally, the prior P(⃗ x|τH), see equation (2), speciﬁes a distribution over a set of points L=
{xν : ν ∈V0}(the leaf nodes of the graph) and speciﬁes their types {τν : ν ∈V0}. These points
are required to lie on the image lattice (e.g.,xν ∈D0). We denote this as {(x,τ(x)) :x∈L} where
τ(x) is speciﬁed in the natural manner (i.e. if x = xν then τ(x) = τν). We specify distributions
P(I(x)|τ(x)) for the probability of the image I(x) at xconditioned on the type of the leaf node.
We specify a default probability P(I(x)|τ0) at positions xwhere there is no leaf node of the object.
This gives a likelihood function for the states ⃗ x= {xν ∈V} of the object model in terms of the
image I = {I(x) :x ∈D0}:
P(I|⃗ x) =
∏
x∈L
P(I(x)|τ(x)) ×
∏
x∈D0/L
P(I(x)|τ0). (3)
The likelihood and the prior, equations (3,2), give a generative model for each object category.
We can extend this in the natural manner to give generative models or two, or more, objects in the
image provided they do not overlap. Intuitively, this involves multiple sampling from the prior to
determine the types of the lattice pixels, followed by sampling from P(I(x)|τ) at the leaf nodes to
determine the image I. Similarly, we have a default background model for the entire image if no
object is present:
PB(I) =
∏
x∈D0
P(I(x)|τ0). (4)
3 Inference by Dynamic Programming
The inference task is to determine which objects are present in the image and to specify their po-
sitions. This involves two subtasks: (i) state estimation, to determine the optimal states of a model
and hence the position of the objects and its parts, and (ii) model selection, to determine whether
objects are present or not. As we will show, both tasks can be reduced to calculating and comparing
log-likelihood ratios which can be performed efﬁciently using dynamic programming methods.
We will ﬁrst describe the simplest case which consists of estimating the state variables of a single
object model and using model selection to determine whether the object is present in the image and,
if so, how many times. Next we show that we can perform inference and model selection for multiple
objects efﬁciently by exploiting part sharing (using hierarchical dictionaries). Finally, we show how
these inference tasks can be performed even more efﬁciently using a parallel implementation. We
stress that we are performing exact inference and no approximations are made. We are simply
exploiting part-sharing so that computations required for performing inference for one object can be
re-used when performing inference for other objects.
3.1 Inference Tasks: State Detection and Model Selection
We ﬁrst describe a standard dynamic programming algorithm for ﬁnding the optimal state of a single
object category model. Then we describe how the same computations can be used to perform model
selection and to the detection and state estimation if the object appears multiple times in the image
(non-overlapping).
Consider performing inference for a single object category model deﬁned by equations (2,3). To
calculate the MAP estimate of the state variables requires computing ⃗ x∗= arg max⃗ x{log P(I|⃗ x) +
log P(⃗ x; τH)}. By subtracting the constant term log PB(I) from the righthand side, we can re-
express this as estimating:
⃗ x∗= arg max
⃗ x
{
∑
x∈L
log P(I(x)|τ(x))
P(I(x)|τ0) +
∑
ν
log P(⃗ xCh(ν)|xν; τν) + logU(xH)}. (5)
5
Figure 3: Left Panel: The feedforward pass propagates hypotheses up to the highest level where
the best state is selected. Center Panel: Feedback propagates information from the top node disam-
biguating the middle level nodes. Right Panel: Feedback from the middle level nodes propagates
back to the input layer to resolve ambiguities there. This algorithm rapidly estimates the top-level
executive summary description in a rapid feed-forward pass. The top-down pass is required to allow
high-level context to eliminate false hypotheses at the lower levels– ”high-level tells low-level to
stop gossiping”.
Here Ldenotes the positions of the leaf nodes of the graph, which must be determined during
inference.
We estimate ⃗ x∗by performing dynamic programming. This involves a bottom-up pass which recur-
sively computes quantities φ(xh,τh) = arg max⃗ x/xh{log P(I|⃗ x)
PB(I) + logP(⃗ x; τh)}by the formula:
φ(xh,τh) = max
⃗ xCh(ν)
{
r∑
i=1
φ(xνi,τνi) + logP(⃗ xCh(ν)|x∗
ν,τν)}. (6)
We refer toφ(xh,τh) as the local evidence for part τh with state xh (after maximizing over the states
of the lower parts of the graphical model). This local evidence is computed bottom-up. We call this
the local evidence because it ignores the context evidence for the part which will be provided during
top-down processing (i.e. that evidence for other parts of the object, in consistent positions, will
strengthen the evidence for this part).
The bottom-up pass outputs the global evidence φ(xH,τH) for object category τHat position xH.
We can detect the most probable state of the object by computing x∗
H= arg maxφ(xH,τH). Then
we can perform the top-down pass of dynamic programming to estimate the most probable states⃗ x∗
of the entire model by recursively performing:
⃗ x∗
Ch(ν) = arg max
⃗ xCh(ν)
{
r∑
i=1
φ(xνi,τνi) + logP(⃗ xCh(ν)|x∗
ν,τν)}. (7)
This outputs the most probable state of the object in the image. Note that the bottom-up process ﬁrst
estimates the optimal ”executive summary” description of the object (x∗
H) and only later determines
the optimal estimates of the lower-level states of the object in the top-down pass. Hence, the algo-
rithm is faster at detecting that there is a cow in the right side of the ﬁeld (estimated in the bottom-up
pass) and is slower at determining the position of the feet of the cow (estimated in the top-down
pass). This is illustrated in ﬁgure (3).
Importantly, we only need to perform slight extensions of this algorithm to compute signiﬁcantly
more. First, we can perform model selection – to determine if the object is present in the image – by
determining if φ(x∗
H,τH) >T , where T is a threshold. This is because, by equation (5), φ(x∗
H,τH)
is the log-likelihood ratio of the probability that the object is present at positionx∗
Hcompared to the
probability that the corresponding part of the image is generated by the background image model
PB(.). Secondly, we can compute the probability that the object occurs several times in the image,
by computing the set {xH : φ(x∗
H,τH) > T, to compute the ”executive summary” descriptions
for each object (e.g., the coarse positions of each object). We then perform the top-down pass
initialized at each coarse position (i.e. at each point of the set described above) to determine the
optimal conﬁguration for the states of the objects. Hence, we can reuse the computations required
to detect a single object in order to detect multiple instances of the object (provided there are no
6
a b
A
b c
B
b
a b
A
c
B
Or
Figure 4: Sharing. Left Panel: Two Level-2 models A and B which share Level-1 model b as a
subpart. Center Panel: Level-1 model b. Inference computation only requires us to do inference
over model b once, and then it can be used to computer the optimal states for models A and B.
Right Panel: Note that is we combine modelsAand Bby a root OR node then we obtain a graphical
model for both objects. This model has a closed loop which would seem to make inference more
challenging. But by exploiting the shape part we can do inference optimally despite the closed loop.
Inference can be done on the dictionaries, far right.
overlaps)7. The number of objects in the image is determined by the log-likelihood ratio test with
respect to the background model.
3.2 Inference on Multiple Objects by Part Sharing using the Hierarchical Dictionaries
Now suppose we want to detect instances of many object categories τH∈MHsimultaneously. We
can exploit the shared parts by performing inference using the hierarchical dictionaries.
The main idea is that we need to compute the global evidence φ(xH,τH) for all objects τH∈MH
and at all positions xHin the top-level lattice. These quantities could be computed separately for
each object by performing the bottom-up pass, speciﬁed by equation (6), for each object. But this
is wasteful because the objects share parts and so we would be performing the same computations
multiple times. Instead we can perform all the necessary computations more efﬁciently by working
directly with the hierarchical dictionaries.
More formally, computing the global evidence for all object models and at all positions is speciﬁed
as follows.
Let D∗
H= {xH∈DHs.t. max
τH∈MH
φ(xH,τH) >TH},
For xH∈D∗
H, let τ∗
H(xH) = arg max
τH∈MH
φ(xH,τH),
Detect ⃗ x∗/xH= arg max
⃗ x/xH
{log P(I|⃗ x)
PB(I) + logP(⃗ x; τH∗(xH))}for allxH∈D∗
H. (8)
All these calculations can be done efﬁciently using the hierarchical dictionaries (except for the max
and arg maxtasks at levelHwhich must be done separately). Recalling that each dictionary element
at level his composed, by part-subpart composition, of dictionary elements at level h−1. Hence
we can apply the bottom-up update rule in equation (6) directly to the dictionary elements. This is
illustrated in ﬁgure (4). As analyzed in the next section, this can yield major gains in computational
complexity.
Once the global evidences for each object model have been computed at each position (in the top
lattice) we can perform winner-take-all to estimate the object model which has largest evidence
at each position. Then we can apply thresholding to see if it passes the log-likelihood ratio test
compared to the background model. If it does pass this log-likelihood test, then we can use the
top-down pass of dynamic programming, see equation (7), to estimate the most probable state of all
parts of the object model.
7Note that this is equivalent to performing optimal inference simultaneously over a set of different generative
models of the image, where one model assumes that there is one instance of the object in the image, another
models assumes there are two, and so on.
7
We note that we are performing exact inference over multiple object models at the same time. This
is perhaps un-intuitive to some readers because this corresponds to doing exact inference over a
probability model which can be expressed as a graph with a large number of closed loops, see
ﬁgure (4). But the main point is that part-sharing enables us share inference efﬁciently between
many models.
The only computation which cannot be performed by dynamic programming are the max and
arg maxtasks at level H, see top line of equation (8). These are simple operations and require
order MH×|DH|calculations. This will usually be a small number, compared to the complexity
of other computations. But this will become very large if there are a large number of objects, as we
will discuss in section (4).
3.3 Parallel Formulation and Inference Algorithm
Finally, we observe that all the computations required for performing inference on multiple objects
can be parallelized. This requires computing the quantities in equation (8).
Figure 5: Parallel Hierarchical Implementation. Far Left Panel: Four Level-0 models are spaced
densely in the image (here an8×2 grid). Left Panel: the four Level-1 models are sampled at a lower
rate, and each have 4 ×1 copies. Right Panel: the four Level-2 models are sampled less frequently.
Far Right Panel: a bird’s eye view of the parallel hierarchy. The dots represent a ”column” of
four Level-0 models. The crosses represent columns containing four Level-1 models. The triangles
represent a column of the Level-2 models.
The parallelization is possible, in the bottom-up pass of dynamic programming, calculations are
done separately for each position x, see equation (6). So we can compute the local evidence for all
parts in the hierarchical dictionary recursively and in parallel for each position, and hence compute
the φ(xH,τH) for all xH∈DHand τH∈MH. The max and arg maxoperations at level Hcan
also be done in parallel for each position xH ∈DH. Similarly we can perform the top-down pass
of dynamic programming, see equation (7), in parallel to compute the best conﬁgurations of the
detected objects in parallel for different possible positions of the objects (on the top-level lattice).
The parallel formulation can be visualized by making copies of the elements of the hierarchical
dictionary elements (the parts), so that a model at level-h has |Dh|copies, with one copy at each
lattice point. Hence at level-h, we have mh ”receptive ﬁelds” at each lattice point in Dh with each
one tuned to a different part τh ∈Mh, see ﬁgure (5). At level-0, these receptive ﬁelds are tuned
to speciﬁc image properties (e.g., horizontal or vertical bars). Note that the receptive ﬁelds are
highly non-linear (i.e. they do not obey any superposition principle)8. Moreover, they are inﬂuenced
both by bottom-up processing (during the bottom-up pass) and by top-down processing (during the
top-down pass). The bottom-up processing computes the local evidence while the top-down pass
modiﬁes it by the high-level context.
The computations required by this parallel implementation are illustrated in ﬁgure (6). The bottom-
up pass is performed by a two-layer network where the ﬁrst layer performs an AND operation (to
compute the local evidence for a speciﬁc conﬁguration of the child nodes) and the second layer
8Nevertheless they are broadly speaking, tuned to image stimuli which have the mean shape of the cor-
responding part τh. In agreement, with ﬁndings about mammalian cortex, the receptive ﬁelds become more
sensitive to image structure (e.g., from bars, to more complex shapes) at increasing levels. Moreover, their
sensitivity to spatial position decreases because at higher levels the models only encode the executive summary
descriptions, on coarser lattices, while the ﬁner details of the object are represented more precisely at the lower
levels.
8
Bottom Up Top Down
Figure 6: Parallel implementation of Dynamic Programming. The left part of the ﬁgure shows
the bottom-up pass of dynamic programming. The local evidence for the parent node is obtained by
taking the maximum of the scores of theCr possible states of the child nodes. This can be computed
by a two-layer network where the ﬁrst level computes the scores for all Cr child node states, which
can be done in parallel, and the second level compute the maximum score. This is like an AND
operation followed by an OR. The top-down pass requires the parent node to select which of the Cr
child conﬁgurations gave the maximum score, and suppressing the other conﬁgurations.
performs an OR, or max operation, to determine the local evidence (by max-ing over the possible
child conﬁgurations)9. The top-down pass only has to perform anarg maxcomputation to determine
which child conﬁguration gave the best local evidence.
4 Complexity Analysis
We now analyze the complexity of the inference algorithms for performing the tasks. Firstly, we
analyze complexity for a single object (without part-sharing). Secondly, we study the complexity
for multiple objects with shared parts. Thirdly, we consider the complexity of the parallel imple-
mentation.
The complexity is expressed in terms of the following quantities: (I) The size |D0|of the image.
(II) The scale decrease factor q(enabling executive summary). (III) The number Hof levels of the
hierarchy. (IV) The sizes {|Mh|: h= 1,..., H}of the hierarchical dictionaries. (V) The number r
of subparts of each part. (VI) The number Cr of possible part-subpart conﬁgurations.
4.1 Complexity for Single Objects and Ignoring Part Sharing
This section estimates the complexity of inference Nso for a single object and the complexity Nmo
for multiple objects when part sharing is not used. These results are for comparison to the complex-
ities derived in the following section using part sharing.
The inference complexity for a single object requires computing: (i) the number Nbu of compu-
tations required by the bottom-up pass, (ii) the number Nms of computations required by model
selection at the top-level of the hierarchy, and (iii) the number Ntd of computations required by the
top-down pass.
The complexity Nbu of the bottom-up pass can be computed from equation (6). This requires a total
of Cr computations for each position xν for each level-h node. There are rH−h nodes at level hand
each can take |D0|qh positions. This gives a total of |D0|CrqhrH−h computations at level h. This
can be summed over all levels to yield:
Nbu =
H∑
h=1
|D0|CrrH(q/r)h = |D0|CrrH
H∑
h=1
(q/r)h = |D0|Cr
qrH−1
1 −q/r{1 −(q/r)H}. (9)
Observe that the main contributions to Nbu come from the ﬁrst few levels of the hierarchy because
the factors (q/r)h decrease rapidly with h. This calculation uses ∑H
h=1 xh = x(1−xH)
1−x . For large H
we can approximate Nbu by |D0|Cr
qrH−1
1−q/r (because (q/r)Hwill be small).
9Note that other hierarchical models, including bio-inspired ones, use similar operations but motivated by
different reasons.
9
We calculate Nms = q|H||D0|for the complexity of model selection (which only requires thresh-
olding at every point on the top-level lattice).
The complexity Ntd of the top-down pass is computed from equation (7). At each level there
are r|H|−h nodes and we must compute Cr computations for each. This yields complexity of∑|H|
h=1 Crr|H|−h for each possible root node. There are at most q|H||D0|possible root nodes (de-
pending on the results of the model selection stage). This yields an upper bound:
Ntd ≤|D0|Crq|H| r|H|−1
1 −1/r{1 − 1
r|H−1|}. (10)
Clearly the complexity is dominated by the complexity Nbu of the bottom-up pass. For simplicity,
we will bound/approximate this by:
Nso = |D0|Cr
qrH−1
1 −q/r (11)
Now suppose we perform inference for multiple objects simultaneously without exploiting shared
parts. In this case the complexity will scale linearly with the number |MH|of objects. This gives
us complexity:
Nmo = |MH||D0|Cr
qrH−1
1 −q/r (12)
4.2 Computation with Shared Parts in Series and in Parallel
This section computes the complexity using part sharing. Firstly, for the standard serial implemen-
tation of part sharing. Secondly, for the parallel implementation.
Now suppose we perform inference on many objects with part sharing using a serial computer.
This requires performing computations over the part-subpart compositions between elements of the
dictionaries. At level hthere are |Mh|dictionary elements. Each can take |Dh|= qh|D|possible
states. The bottom-up pass requires performing Cr computations for each of them. This gives a
total of ∑H
h=1 |Mh|Cr|D0|qh = |D0|Cr
∑H
h=1 |Mh|qh computations for the bottom-up process.
The complexity of model selection is |D0|qH×(H+ 1)(this is between all the objects, and the
background model, at all points on the top lattice). As in the previous section, the complexity of the
top-down process is less than the complexity of the bottom-up process. Hence the complexity for
multiple objects using part sharing is given by:
Nps = |D0|Cr
H∑
h=1
|Mh|qh. (13)
Next consider the parallel implementation. In this case almost all of the computations are performed
in parallel and so the complexity is now expressed in terms of the number of ”neurons” required to
encode the dictionaries, see ﬁgure (5). This is speciﬁed by the total number of dictionary elements
multiplied by the number of spatial copies of them:
Nn =
H∑
h=1
|Mh|qh|D0|. (14)
The computation, both the forward and backward passes of dynamic programming, are linear in the
number Hof levels. We only need to perform the computations illustrated in ﬁgure (6) between all
adjacent levels.
Hence the parallel implementation gives speed which is linear in Hat the cost of a possibly large
number Nn of ”neurons” and connections between them.
4.3 Advantages of Part Sharing in Different Regimes
The advantages of part-sharing depend on how the number of parts |Mh|scales with the level h
of the hierarchy. In this section we consider three different regimes: (I) The exponential growth
10
regime where the size of the dictionaries increases exponentially with the levelh. (II) The empirical
growth regime where we use the size of the dictionaries found experimentally by compositional
learning [19]. (III) The exponential decrease regime where the size of the dictionaries decreases
exponentially with levelh. For all these regimes we compare the advantages of the serial and parallel
implementations using part sharing by comparison to the complexity results without sharing.
Exponential growth of dictionaries is a natural regime to consider. It occurs when subparts are
allowed to combine with all other subparts (or a large fraction of them) which means that the number
of part-subpart compositions is polynomial in the number of subparts. This gives exponential growth
in the size of the dictionaries if it occurs at different levels (e.g., consider the enormous number of
objects that can be built using lego).
An interesting special case of the exponential growth regime is when |Mh|scales like 1/qh, see
ﬁgure (7)(left panel). In this case the complexity of computation for serial part-sharing, and the
number of neurons required for parallel implementation, scales only with the number of levels H.
This follows from equations (13,14). But nevertheless the number of objects that can be detected
scales exponentially as qM. By contrast, the complexity of inference without part-sharing scales
exponentially with q, see equation (12, because we have to perform a ﬁxed number of computations,
given by equation (11), for each of an exponential number of objects. This is summarized by the
following result.
Result 1: If the number of shared parts scales exponentially by |Mh|∝ 1
qh then we can perform
inference for order qH objects using part sharing in time linear in H, or with a number of neu-
rons linear in Hfor parallel implementation. By contrast, inference without part-sharing requires
exponential complexity.
To what extent is exponential growth a reasonable assumption for real world objects? This motivates
us to study the empirical growth regime using the dictionaries obtained by the compositional learning
experiments reported in [19]. In these experiments, the size of the dictionaries increased rapidly at
the lower levels (i.e. small h) and then decreased at higher levels (roughly consistent with the
ﬁndings of psychophysical studies – Biederman, personal communication). For these ”empirical
dictionaries” we plot the growth, and the number of computations at each level of the hierarchy, in
ﬁgure (7)(center panel). This shows complexity which roughly agrees with the exponential growth
model. This can be summarized by the following result:
Result 2: If |Mh|grows slower than 1/qh and if |Mh|< rH−h then there are gains due to part
sharing using serial and parallel computers. This is illustrated in ﬁgure (7)(center panel) based on
the dictionaries found by unsupervised computational learning [19]. In parallel implementations,
computation is linear in Hwhile requiring a limited number of nodes (”neurons”).
Finally we consider the exponential decrease regime. To motivate this regime, suppose that the
dictionaries are used to model image appearance, by contrast to the dictionaries based on geometrical
features such as bars and oriented edges (as used in [19]). It is reasonable to assume that there are a
large number of low-level dictionaries used to model the enormous variety of local intensity patterns.
The number of higher-level dictionaries can decrease because they can be used to capture a cruder
summary description of a larger image region, which is another instance of the executive summary
principle. For example, the low-level dictionaries could be used to provide detailed modeling of
the local appearance of a cat, or some other animal, while the higher-level dictionaries could give
simpler descriptions like ”cat-fur” or ”dog-fur” or simply ”fur”. In this case, it is plausible that the
size of the dictionaries decreases exponentially with the level h. The results for this case emphasize
the advantages of parallel computing.
Result 3: If |Mh|= rH−h then there is no gain for part sharing if serial computers are used, see
ﬁgure (7)(right panel). Parallel implementations can do inference in time which is linear in Hbut
require an exponential number of nodes (”neurons”).
Result 3 may appear negative at ﬁrst glance even for the parallel version since it requires an expo-
nentially large number of neurons required to encode the lower level dictionaries. But it may relate
to one of the more surprising facts about the visual cortex in monkeys and humans – namely that the
ﬁrst two visual areas, V1 and V2, where low-level dictionaries would be implemented are enormous
compared to the higher levels such as IT where object detection takes places. Current models of V1
and V2 mostly relegate it to being a large ﬁlter bank which seems paradoxical considering their size.
11
Level (h)
Mh ∝ 1
qh
Amount of computation
 
 
Shared Parts
No Sharing
(a)
Level (h)
Amount of computation
q = 1/3, r = 2
 
 
Shared
No Sharing (b)
Level (h)
Mh = MHrH−h
Amount of computation
different values of r (c)
Figure 7: The curves are plotted as a function of h. Left panel: The ﬁrst plot is the case where
Mh = a/(qh). So we have a constant cost for the computations, when we have shared parts. Center
panel: This plot is based on the experiment of [19]. Right panel: The third plot is the case where
Mh decreases exponentially. The amount of computation is the same for the shared and non-shared
cases. A set of plots with different values of r.
For example, as one theorist [12] has stated when reviewing the functions of V1 and V2 “perhaps
the most troublesome objection to the picture I have delivered is that an enormous amount of cortex
is used to achieve remarkably little”. Our complexity studies suggest a reason why these visual areas
may be so large if they are used to encode dictionaries10.
5 Discussion
This paper provides a complexity analysis of what is arguably one of the most fundamental problem
of visions – how, a biological or artiﬁcial vision system could rapidly detect and recognize an enor-
mous number of different objects. We focus on a class of hierarchical compositional models [20, 19]
whose formulation makes it possible to perform this analysis. But we conjecture that similar results
will apply to related hierarchical models of vision (e.g., those cited in the introduction).
Technically this paper has required us to re-formulate compositional models so that they can be
deﬁned on regular lattices (which makes them easier to compare to alternatives such as deep belief
networks) and a novel parallel implementation. Hopefully the analysis has also clariﬁed the use of
part-sharing to perform exact inference even on highly complex models, which may not have been
clear in the original publications. We note that the re-use of computations in this manner might
relate to methods developed to speed up inference on graphical models, which gives an interesting
direction to explore.
Finally, we note that the parallel inference algorithms used by this class of compositional models
have an interesting interpretation in terms of the bottom-up versus top-down debate concerning pro-
cessing in the visual cortex [4]. The algorithms have rapid parallel inference, in time which is linear
in the number of layers, and which rapidly estimates a coarse “executive summary” interpretation
of the image. The full interpretation of the image takes longer and requires a top-down pass where
the high-level context is able to resolve ambiguities which occur at the lower levels. Of course, for
some simple images the local evidence for the low level parts is sufﬁcient to detect the parts in the
bottom-up pass and so the top-down pass is not needed. But more generally, in the bottom-up pass
the neurons are very active and represent a large number of possible hypotheses which are pruned
out during the top-down pass using context, when “high-level tells low-level to stop gossiping”.
Acknowledgments
Many of the ideas in this paper were obtained by analyzing models developed by L. Zhu and Y .
Chen in collaboration with the ﬁrst author. G. Papandreou gave very useful feedback on drafts of
this work. D. Kersten gave patient feedback on speculations about how these ideas might relate to
10Of course, this is extremely conjectural.
12
the brain. The WCU program at Korea University, under the supervision of S-W Lee, gave peace
and time to develop these ideas.
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Peer review:
[Review]:
This paper explores how inference can be done in a part-sharing model and the computational cost of doing so. It relies on 'executive summaries' where each layer only holds approximate information about the layer below. The authors also study the computational complexity of this inference in various settings.

I must say I very much like this paper. It proposes a model which combines fast and approximate inference (approximate in the sense that the global description of the scene lacks details) with a slower and exact inference (in the sense that it allows exact inference of the parts of the model). Since I am not familiar with the literature, I cannot however judge the novelty of the work.

Pros:
- model which attractively combines inference at the top level with inference at the lower levels
- the analysis of the computational complexity for varying number of parts and objects is interesting

- the work is very conjectural but I'd rather see it acknowledged than hidden under toy experiments.
Cons: