Mining Visual Phrases for Long-Term Visual SLAM
Tanaka Kanji

Chokushi Yuuto

Ando Masatoshi

Abstract— We propose a discriminative and compact scene
descriptor for single-view place recognition that facilitates
long-term visual SLAM in familiar, semi-dynamic and partially changing environments. In contrast to popular bag-ofwords scene descriptors, which rely on a library of vector
quantized visual features, our proposed scene descriptor isbased on a library of raw image data (such as an available
visual experience, images shared by other colleague robots,and publicly available image data on the web) and directlymine it to find visual phrases (VPs) that discriminatively
and compactly explain an input query / database image. Our
mining approach is motivated by recent success in the fieldof common pattern discovery—specifically mining of common
visual patterns among scenes—and requires only a single
library of raw images that can be acquired at different time orday. Experimental results show that even though our scene
descriptor is significantly more compact than conventionaldescriptors it has a relatively higher recognition performance.

I. INTRODUCTION
Long-term visual SLAM, in familiar, semi-dynamic, andpartially changing environments is an important area of
research in robotics [1]–[8]. The SLAM task can be viewed
as a combination of two subtasks: visual map building
and robot self-localization. A robot incrementally builds anenvironment map using images viewed during visual robot
navigation, while simultaneously using the map to localize
itself with respect to the environment. These two subtasks
respectively involve incremental construction and retrieval ofa database of view images. The view image retrieval process
is the primary focus of this paper (Fig.1).
In this paper, we focus on a compact discriminative scene
descriptor for single-view place recognition. Unlike typicallong-term SLAM scenarios that rely on the assumptionof view sequence measurements (e.g., SeqSLAM [1]), we
tackle the challenging task of single-view place recognition
with important applications, in which the robot’s views
only sparsely overlap with pre-mapped views. The main
problem we faced is the question of how to describe ascene discriminatively and compactly—both of which arenecessary in order to cope with changes in appearance anda large amount of visual information.
We address this issue by mining visual phrases. In thefield of computer vision, visual phrase is a method used toenhance the discriminative power of visual features, whereco-located features in a visual image are grouped together
to form a visual phrase [9]–[11]. In contrast to popularbag-of-words scene descriptors, which rely on a library of
Our work has been supported in part by JSPS KAKENHI Grant-in-Aid forYoung Scientists (B) 23700229, and for Scientific Research (C) 26330297.
K. Tanaka, Y. Chokushi, and M. Ando are with Faculty of Engineering,
University of Fukui, Japan. tnkknj@u-fukui.ac.jp

Fig. 1. Modeling and matching a pair of scene images (“query”, “database”)
using our scene descriptor. A raw image matching process (“CPD”) mines anavailable visual experience (“known reference image”) to find discriminativevisual phrases that effectively explain an input query / database image. The
scene matching problem then becomes one of comparing reference image
ID and bounding boxes between query and database scenes.

vector quantized visual features (e.g., FAB-MAP [12]), ourproposed scene descriptor is based on a library of raw image
data (such as an available visual experience, images sharedby other colleague robots, and publicly available image dataon the web) and directly mines it to find visual phrases(VPs) that discriminatively and compactly explain an input
query/database image. Our mining approach is motivated byrecent success in the field of common pattern discovery(CPD) [13]–[15], specifically, mining of common visual
patterns among scenes, and requires only a single library of
raw images that can be acquired at different time or day. In
contrast to typical supervised VP learning frameworks [13],our mining approach is unsupervised, and thus enables arobot to learn a compact and discriminative scene model
without human intervention. Our implementation of CPD
is inspired by the robust high-speed CPD algorithm andrandomized visual phrase (RVP) in [13]. The results ofevaluations of our method conducted in challenging visual
image retrieval experiments and performance comparisonswith the conventional Bag-of-Visual-Features and FAB-MAP
techniques show that even though our scene descriptor issignificantly more compact, it has a higher recognition
performance than these techniques.

II. RELATED WORKS
Existing approaches to long-term SLAM are broadly divided into those that describe a variety of visual appearancesof scenes in a single map and those in which multiple
independent maps are employed to describe different visualexperiences. [1] developed a robust state-of-the-art SLAM
framework, called SeqSLAM, for cross-season navigation
tasks separated by months or years and opposite seasons.
However, the SeqSLAM algorithm explicitly assumes thatimage sequence measurements are available for robot localization and relies on an image sequence-based scene
descriptor. [2] proposed a robust approach that can capture
the typical time-varying appearance of an environment in
multiple different maps, with the number of experiences
required tending to a constant. [3] showed that by quantizing
local features in both feature and image space, discriminative
statistics can be gained on the co-occurrences of features atdifferent times throughout the day. However, it is not clear
whether these approaches can create compact map representations because they directly memorize multiple varieties ofvisual experiences. A notable exception is [4], in which theissue of compactness is addressed with a question: “Howlittle and what quality of visual information is needed to
localize along a familiar route?” Although impressive results
have been demonstrated, it also relies on the assumption ofimage sequence measurements.
Scene descriptors for SLAM problems have been studied
extensively. Global feature approaches such as SeqSLAM[1] (in which a scene is represented by a single globalfeature vector) are compact and have high matching speeds;
however, they are not robust, and often require image sequence measurements. Local feature approaches have longoutperformed global feature approaches. For example, FABMAP [12], in which a scene is represented and matched as abag-of-visual-features (BoVF), is one of the most successfulalgorithms. However, BoVF-based methods suffer from highspatial costs because they represent a scene using numerous
small local features [16], and each feature consumes several
bits, even when a succinct inverted file system is being used.
Other related work include scene models (such as spatial
context [13]–[15], part model [17]–[19], and object model[20]–[22]) designed for specific computer vision applications. While these approaches achieve accurate recognition
with compact representation of view images, they all assume
some level of human supervision in assembling training
datasets and learning VP/part/object detectors. Our proposed
method differs from these scene models in the formulationof the problem. In our formulation, visual phrases are discovered via a CPD method, and represented in the form ofVPs, which enables an autonomous robot to learn a compact
scene model without human intervention.
Long-term SLAM for changing environments is a rapidly
growing area of research [23]–[26]. [23] presented a framework that uses the stored distinct visual appearances of aworkspace, i.e., visual experiences, to improve localization
on future visits, and introduced a novel introspective process,executed between sorties. [24] presented a visual mapping
system that uses only the input from a stereo camera, andwhich continually updates an optimized metric map in large
indoor spaces with movable objects, e.g., people, furniture,
partitions. [25] investigated the persistent navigation andmapping problem over a two-week period in the context
of an autonomous robot that performs mock deliveries in aworking office environment, and presented a solution basedon the biologically inspired visual SLAM system, RatSLAM.
[26] investigated the robustness of the place recognition of
the SeqSLAM algorithms in changing environments acrossall four seasons on a 3000 km journey. In contrast, we focus
on the issue of visual experience mining for unsupervised
descriptor learning.
Our earlier works also focused on scene descriptors, (e.g.,
global GIST feature descriptor [27], local shape context
descriptor [28], and part-based scene descriptor [29]) and
SLAM in changing environments (e.g., map updating [30]and change detection [31]). In this paper however, we focus
on the discriminativity and compactness of scene descriptors
and the problem of long-term SLAM.
III. APPROACH
SLAM’s two main subtasks, visual map building androbot self-localization, involve incremental construction andretrieval of a database of view images, respectively:
1) Either subtask interprets each view image in the queryor the database as a scene descriptor, and then2) the localization subtask searches the database to findsimilar descriptors to the query descriptor.
Then, the image with the highest similarity score is viewed as
the localized image. These subtasks are respectively outlinedin Alg.1 and Alg.2, and discussed in detail in the subsections
below.
Algorithm 1 Mapping for Our Model
Input: Input image I , and reference images {R j }Lj=1 .
1: Compute bag-of-visual-features W of the input image I .
2: Retrieve most similar reference images {R j }Jj=1 to I .
3: for j = 1 to J do
4:
Sample subimages {Ii }Ii=1 from the input image I .
5:for i = 1 to I do
6:
Perform CPD between Ii and R j .
7:
Crop bounding box Bi, j .
8:
end for
9: end for

For the above interpretation, we assume that a dictionary
or a library of random L reference view images is given.
The reference images are not required to be associated withspatial information such that the viewpoint and orientation
are known. Such images are cheaper than the mapped images
with spatial information required by the map database, andare more readily available. For example, they can be avisual experience obtained by the robot itself in a previous
navigation, or shared by other colleague robots, e.g., via information sharing networks [5]. They could also be publicly
available resource image data on the web, such as Google

Algorithm 2 Localization for Our Model
Input: Input image I , and reference images {R j }Lj=1 .
1: Compute bag-of-visual-features W of the input image I .
2: Retrieve most similar reference images {R j }Jj=1 to I .
3: for j = 1 to J do
4:
Sample subimages {Ii }Ii=1 from the input image I .
5:for i = 1 to I do
6:
Perform CPD between Ii and R j .
7:
Crop bounding box Bi, j .
8:
end for
9: end for
10: for all database image I ′ do11:
Look up bounding box representation {Bi,′ j } of I ′ .
12:Compute image level similarity f (I , I ′ ).
13: end forStreetView. A small subset of J appropriate reference images
most similar to a given input image are selected and used
to interpret the image. Our experimental results suggest thathigh localization performance tends to be associated withcoverage of the robot’s route by these library images. We
discuss the view image library issue in Section III-A.
Next, we perform CPD between an input and the reference
images to mine a set of VPs that effectively explain the
input image. Any CPD algorithm can be adopted, but forour purposes, we utilize the RVP algorithm [13] because
it provides fast and stable detection of common visual
patterns and can generally handle scale variations among
objects without relying on any image segmentation or region
detection. We describe the CPD algorithm used in Section
III-B.
Next, we obtain a bag-of-bounding-boxes (BoBB) representation, which consists of J pairings of• a reference image ID (an integer),• I visual phrases (BBs on the reference image),as a scene descriptor. Because a BB is a much lowerdimensional representation than many existing feature descriptors such as 128 dimensional SIFT vectors, the searchfor similar BBs to a query BB can be done quite quickly.
We discuss scene descriptors in Section III-C.
A. Mining Visual Experience
To select the most similar J reference images {R j }Jj=1 toa given input image, we perform similarity search over the
library of L reference images. The similarity search algorithm
is designed on the bag-of-raw-features image model [32], inwhich every image is modeled as an unordered collectionof raw feature vectors (such as SIFT visual features). The
pairwise similarity between the input and a reference images
is evaluated as the number of similar SIFT matches between
the image pair. Approximate near neighbor search (ANN)[33] can be used to efficiently search for similar SIFTs to an
input query SIFT. The similarity search is iterated for every
SIFT feature in the input image, and those J similar images
that are supported by the highest number of SIFT matches
are considered as the relevant reference images (Alg.1 Line2, Alg.2 Line 2).

Unlike the popular bag-of-words image model, our similarity search does not rely on quantized SIFT vectors.
Instead, our image model is based on the precise, raw SIFT
features. Although it is computationally more demanding,the search process is fast because our model only requires asmall library.
B. Mining Visual Phrases
We adopt the RVP algorithm (Alg.3) to mine a set of VPs
that effectively explain an input image. The RVP algorithm
addresses the problem of common object search over reference images given an image of the target object [13]. In its
implementation, a set of I subimages {Ii }Ii=1 are randomly
sampled from the input image I . Then, each subimage Ii
is viewed as a hypothesized target image for the problemof object search over a reference image R j . Although initial
localization of such a randomly sampled target sub-image
within the input image may be inaccurate, the target subimage is recropped and expected to be sufficiently localized bymeans of the RVP algorithm, as shown below.
Algorithm 3 Common Pattern Discovery
Input: Input image I , and reference image R.
1: for all pixel p on the voting image V do
2:
Initialize the pixel value V (p) to 0.
3: end for
4: Look up bag-of-words histogram H of the input image I .
5: for k = 1 to K do
6:
Partition reference image R into M × N patches {Rm,n,k }.
7:
for m = 1 to M do
8:for n = 1 to N do
9:Look up bag-of-words histogram Hm,n,k of Rm,n,k .
10:Compute similarity Sm,n,k between H and Hm,n,k .
11:
for all pixel that belongs to the patch p ∈ Rm,n,k do
12:V (p) ← V (p) + Sm,n,k .
13:
end for14:
end for15:
end for
16: end for

The RVP algorithm employs a bag-of-visual-words image
representation style, and thus requires a visual word vocabulary. In our approach, we use the set of V visual featuresin the library images as the vocabulary. To translate a given
visual feature, we run the ANN to find similar visual featuresin the library, followed by a verification step to ensure thatthe normalized L1-distance between the SIFT descriptor pairis smaller than 0.4. We then assign their feature IDs as thevisual words, i.e., multiple visual word per feature.
We then independently and randomly partition reference
image R a total of K times into M × N non-overlapping
rectangular patches (Alg.3 Line 6). The result is a pool of
M × N × K image patches (in our experiments, 32 × 16 × 200
patches) or visual phrases {Rm,n,k | m ∈ [1, M], n ∈ [1, N],k ∈ [1, K]} each of which is characterized as a V -dimensional
histogram, Hm,n,k , recording the visual word frequency ofRm,n,k (Alg.3 Line 9). Since in the k-th partition, each pixel,t, falls into a single patch, {Pm,n,k | t ∈ Pm,n,k , k ∈ [1, K]},
there are a total of K patches containing t after K rounds

of partitioning. Each patch is viewed as a VP, and provides
more contextual information than a typical visual word. For
each pixel, t, the confidence that the pixel is part of thetarget object is measured by the average similarity between
the input, Ii , and the visual phrase, Rm,n,k , over all the K
patches that contain t (Alg.3 Lines 10-13).
Following the assignment of a confidence score to eachpixel, we obtain a voting map for each image pair, Ii , R j .
Object localization then becomes the task of segmenting thedominant region in the form of bounding box Bi,∗ j fromthe reference image of interest, R j . Intuitively, the optimal
bounding box should be the one whose sum of confidence
scores over all the pixels inside the bounding box are higher
than all other potential bounding boxes. Further, the integral
image [34] can be used to efficiently compute the sum of thevalues in the rectangular regions defined by these boundingboxes. The size of a bounding box should be sufficiently
small that it can be localized well, and should not exceed
10% of the area of the reference image.
C. Scene Descriptor
The BoBB scene descriptor consists of J pairings of a reference image ID (i.e., an integer) and a set of I visual phrases(BBs on the reference image). A BB carries appearance information of a VP as it indicates the VP region within the reference image. Suppose that a function Overlap(Bi, j , Bi′′ , j′ )returns the area of overlap between a given BB pair Bi, j ,Bi′′ , j′ when they belong to the same reference image or zerootherwise. Note that our current implementation ensures thateach bounding box is well localized, i.e., smaller than 10%of the image area, and we have already found that there
is no need to penalize the size of the bounding boxes. A
large value for Overlap(Bi, j , Bi′′ , j′ ) indicates that the VPs
cropped by the BBs are similar between the image pair, andvice versa. By aggregating the VP-level similarity, we obtain
the image-level similarity (Alg.2 Line 12):f (I , I ′ ) =1 J IOverlap(Bi, j , Bi′′ , j′ ).
∑ ∑ max′ , j′
IJ j=1ii=1required to describe a given path. The sequences start at
10 different locations inside the university campus—somegoing through the main central path, and others going along
the pedestrian walkway along the campus wall, as shown in
the figure. Occlusion is severe in all the scenes, and people
and vehicles are dynamic entities occupying the scene. In
order to evaluate the performance of the framework, wetraversed each path twice, and acquired a pair of view image
sequences for mapping and localization for each path. A
random collection of 100 view images over various days
and times were acquired along each path, and used as thesize L = 100 library of reference images. In addition, achallenging “cross season” dataset, in which the input andthe reference images were acquired in different seasons, wasalso created and utilized. Fig.3 shows samples of the view
images used in the evaluation.
For CPD, our method selected a set of J = 4 reference
images from the size L = 100 library and learned I = 4 VPs
for each reference image, based on the SIFT similarity searchand the CPD. Fig.4 shows examples of the results obtained. It
can be seen that common VPs were successfully discovered
for the relevant image pairs. Even when there was no identical object between the input and the reference images, our(1)

Since a BB can be compactly represented by a 4D parameter(a much lower-dimensional representation than other local
feature descriptors such as 128D SIFT vectors), the searchfor BBs similar to a query BB can be conducted very rapidly.

Fig. 2. Experimental environments and viewpoint paths (Bird’s eye view).
a,b,c,d,e,f,g,h,i,j: 10 paths used for quantitative evaluation. CS: path used
for the “cross season” case.

IV. EXPERIMENTAL EVALUATIONIn this section we present and discuss the results ofexperiments conducted using the proposed framework.
The dataset used in the experiments consisted of sequencesof view images taken around a university campus, usinga handheld camera as a monocular vision sensor. 1 Fig.2
is a bird’s eye view of our experimental environments andviewpoint paths. We considered a typical scenario that deals
with view images that are taken relatively far apart (1 m5 m) from each other, significantly reducing the memory
1 Note that there is nothing in our algorithm that requires sequential image
sequences—in contrast to typical SLAM algorithms such as SeqSLAM. We
chose to use the image sequence only for the convenient presentation.

input images

reference images“cross season” images

Fig. 3.

Samples of view images used for evaluation.

RVP-based method robustly and efficiently detected similar
objects by aggregating the patch-level similarity between theimage pair.
We evaluated the proposed VP method (“VP”) in termsof the retrieval accuracy and compared the results withthat obtained by bag-of-visual-features (“BoVF”) and FABMAP 2.0 (“FAB-MAP”) [12]. For BoVF, we weighted theoriginal BoVF vectors using a standard TF-IDF weighting
scheme, and a vocabulary with 16K words. For FAB-MAP,
we used the same code used by the authors in [12]. We
conducted a series of 100 independent retrievals for eachof 100 random query images and for each of the 10 different paths. Retrieval performance is measured in terms of
averaged normalized rank (ANR)—a ranking-based retrieval
performance measure, where the smaller value is better—in percent (%). To evaluate ANR, we evaluated the rank
assigned to the ground-truth relevant image for each of the
100 independent retrievals, and then normalized the rankwith respect to the database size and computed the averageover the 100 retrievals.
In Fig.5, it can be seen that our approach outperforms
BoVF and FAB-MAP in most of the retrievals considered
here, even though our BoBB descriptor is significantly morecompact. The result for BoVF is not as impressive as we
had expected. Matched features often occupy only a small
portion of an image, and as a result, are difficult for the
BoVF method to be identify. In contrast, our matched VPbased framework achieves much better retrieval performance

s1

c1

s2

c2

s3

c3

s4

c4

Fig. 4. Examples of common pattern discovery (CPD). s1-4: The single
season case. c1-4: The “cross season” case. For each panel, the top row
shows CPD for a query image and the bottom row shows CPD for the
ground truth database image. For each panel, the left column shows the
input image, the middle column shows the reference image selected for
CPD, and the right column shows the CPD results, i.e., voting map andBB.

Fig. 5.

Fig. 6.

Quantitative performance.

Results for various #reference images.

while requiring only IJ = 16 indexes per image.
Fig. 6 shows the ANR performance for various setting of
the #reference images parameters. It is clear that good andstable results are obtained when the number of reference
images is sufficiently large. The larger number of reference
images tend to have better common VPs.
Fig. 7 illustrates the benefits of utilizing both the reference
image IDs and the pose and the shape of the bounding boxes(BBs) for scene description. In the figure, the results of CPD,
i.e., the reference image IDs and the pose and the shape ofBBs, are plotted on a single 2D plane for a short sequence ofview images. It can be seen that although the reference image
IDs are not sufficiently discriminative, the pose and shape of
the BBs provide additional discriminative information, thatcan be captured by our bounding box -based scene descriptor.
Fig. 8 shows how the sensitivity of retrieval performancecorrelates with the choice of library. In this experiment, weare particularly interested in understanding the impact of the
choice of the library on the performance. It is clear that theuse of visual experiences from different viewpoint paths is

Fig. 7. Selected reference images and their bounding boxes. Top: BBs forreference images. For visualization, the BB for each n-th reference image
is normalized to fit within an area [n − 1, n]×[0, 1]. Bottom: x10 close-up
for x ∈ [0, 10].

Fig. 8.
Sensitivity of retrieval performance to the choice of library.
The five libraries (a)-(e) are used to explain two different sets of queryand database images. “hetero”: performance when using the library fromdifferent viewpoint path. (·) indicates the path ID of the library.

not very effective. Because our method is designed to explain
an input image using a pool of cropped reference images, it
is not suitable for general cases in which entire regions of the
input image are not similar to any reference image. We plan
to derive a means of automatically and adaptively choosing
the library for a given set of database images in future work.
Fig. 9 visualizes the frequency of each reference image
being selected, and gives examples of the most and least
frequently selected reference images overlaid with all thebounding boxes. As can be seen from the figure, the reference
images where non-common objects or non-discriminative
objects such as trees occupy the entire image region tend
to be less frequently selected than other reference images.
Further, it shows that the frequency of the most frequent
reference image is 10 times higher than that of the lessfrequent 50% of images in the library.
We also evaluated the methods on a challenging “crossseason” scenario. The database and reference image sequences were acquired at different time of day in theautumn, while the query image sequence was acquired in thewinter. Further the sequences were acquired in very different
illumination conditions and changes in appearance due tofresh snow cover (see Fig.3). Fig. 10 shows the results forthe proposed framework compared with FAB-MAP. It canbe seen that the proposed method is more robust despite the6frequency [%]

5
4
3
2
1
0
0

10

20

30

40

50
sorted ref ID

60

70

80

90

100

a

b

c

Fig. 9. Example results of selecting reference images. (a) Frequency ofeach reference image being selected. (b/c) The four most / least frequent
reference images overlaid with all the bounding boxes.

Fig. 10.

Performance on “cross season” case.

difficult conditions. Even though entire regions were often
dissimilar between images, parts of them often similar andwere captured by our VP-based approach.
The advantage of our approach is even more obvious froma spatial cost point of view. For example, when I, J = 4,
L = 100, our scene descriptor consists of four reference
image IDs, each of which is represented by 22 bits (i.e.,log2 LCJ ), and IJ = 16 bounding boxes. This is extremelycompact, even when compared with other compact local
feature approaches such as BoVF and FAB-MAP, wherean image is typically represented by numerous words or
entries to the inverted file system, each of which consumes
a few bits. Even global feature approaches such as the GIST
feature descriptor still consume thousands of bits per image.
A notable exception is those that rely on advanced vector
quantization techniques, such as VLAD feature descriptor
and the compressed GIST global feature employed in ourearlier work. Discriminativity preserving vector quantization
for further compacting the proposed BoBB scene descriptor
will be addressed in future work.
V. DISCUSSIONSOur experimental results suggest that high localization
performance tends to be associated with coverage of therobot’s route by these library images. In future, we plan
to investigate how comprehensively do these library images
need to cover the path. For example if any building is
missing in the library set, it is more difficult to produce
meaningful results. We are also interested in investigating
how different library sizes (e.g., L = 10, 20, 50) affect the
localization performance. Combining the proposed visualphrase framework with traditional visual word framework,i.e., bag-of-words, can effectively address the coverage area
issue. When no sufficient match is found by the proposed
CPD technique, it can be viewed that the scene cannot be
explained by the available visual experience. Such a scene
might be better explained by the more primitive, visual
words, at the cost of increased space for storing the visual
words.
The current implementation of the framework is primarily
aimed at demonstrating the effectiveness of the CPD techniques for compact and discriminative scene modeling. The
bottleneck for a real-time application would be the speedof the CPD processing. The time complexity of CPD islinear to the number of candidate library images that are
output by the visual experience mining. To address thetime cost for CPD, we plan to leverage more advanced
mining algorithms, instead of the simple ANN algorithm

used in our current study, to achieve better scalability forincreasing numbers of library images. A key observation isthat we can reformulate the visual experience mining as aself-localization problem, i.e., localizing the viewpoint withrespect to a map of library images [29], and thus leverage thelarge body of self-localization algorithms in the literature. In
future, we plan to expand in this direction.
VI. CONCLUSIONSIn this paper, we proposed a visual phrase approach tothe problem of view image retrieval in partially changing
environments. The main novelty of this approach lies in
the fact that common visual phrases are mined in an unsupervised manner via CPD, and can be used for compact
characterization and efficient retrieval of view images. This
contrasts with the existing supervised frameworks with prelearned visual phrases commonly found in the field ofcomputer vision. Our novel approach enables a robot visionsystem to learn a compact and semantic scene model without
requiring human intervention. Another novelty lies in the useof a bounding box -based phrase annotation scheme for acompact and discriminative scene descriptor. Experimental
results and performance comparisons with existing BoVF
and FAB-MAP frameworks show that it is possible to havehigh retrieval performance despite the fact that our scene
descriptor is significantly more compact.
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