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IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 8, DECEMBER 2013

From Logo to Object Segmentation
Fanman Meng, Hongliang Li, Senior Member, IEEE, Guanghui Liu, and King Ngi Ngan, Fellow, IEEE

Abstract—This paper proposes a method to segment object fromthe web images using logo detection. The method consists of threesteps. In the first step, the logos are located from the original images by SIFT matching. Based on the logo location and the object shape model, the second step extracts the object boundaryfrom the image. In the third step, we use the object boundary tomodel the object appearance, which is then used in the MRF based
segmentation method to finally achieve the object segmentation.
The key of our method is the object boundary extraction, which isachieved by searching a variation of the shape model that best fits
the local edge of the image. Affine transform is used to consider thevariations among the objects. Meanwhile, the Nelder-Mead simplex method with a simple initial rough search is used to run theboundary search. To verify the proposed method, we collect a LogoSeg dataset from the web such as Flickr and Google. The MOMI
dataset is also used for the verification. The experimental results
demonstrate that the proposed logo detection based segmentation
method can improve the performance of the object segmentation.
Index Terms—Specific object segmentation, logo detection.

I. INTRODUCTIONI

MAGE segmentation is a fundamental task in manyhigh-level computer vision tasks, such as scene understanding [1], image retrieval [2], and object recognition [3].
In the past few decades, many image segmentation methods
have been proposed, which can be roughly classified into
two categories: non-semantic object segmentation [4], [5] andsemantic object segmentation. The first one intends to extract
some uniform and homogeneous regions from the image withrespect to texture or color properties, such as superpixels based
segmentation method [4], [5]. The second one instead extracts
the semantic object from the images, which can provide thesemantic prior of the object and benefit the high-level computer
vision tasks. Meanwhile, semantic object segmentation remains
challenging due to the difficulty of generating the semantic
concepts by low-level features.
Manuscript received September 17, 2012; revised February 24, 2013; accepted May 31, 2013. Date of publication September 05, 2013; date of current version November 13, 2013. This work was supported in part by the National Natural Science Foundation of China under Grants No. 61271289, The
Ph.D. Programs Foundation of Ministry of Education of China under Grants No.
20110185110002, and the Fundamental Research Funds for the Central Universities under Grants E022050205. The associate editor coordinating the reviewof this manuscript and approving it for publication was Prof. Wenwu Zhu.
F. Meng, H. Li, and G. Liu are with School of Electronic Engineering,
University of Electronic Science and Technology of China, Cheng Du
610073, China (e-mail: fanmanmeng@gmail.com; hlli@uestc.edu.cn;
guanghuiliu@uestc.edu.cn).
K. N. Ngan is with Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, and also with the School of Electronic Engineering, University of Electronic Science and Technology of China,
Chengdu 610073 China (e-mail: knngan@ee.cuhk.edu.hk).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TMM.2013.2280893

Fig. 1. Examples of the specific object (commodity) segmentation. Starbucks,
Fuwa and FedexTrucks are shown in the first, second and third rows, respectively. The corresponding logos are shown in the last column.

Specific object segmentation is a semantic object segmentation method, which aims to extract a specific object from the images. For example, the commodities (the specific objects) may
be required to be segmented from the retrieved images as shownin Fig. 1. The existing unsupervised or weakly supervised segmentation methods have been used to achieve the specific object
extraction, such as co-segmentation [6]–[12] and weakly supervised object segmentation [13]–[15]. In these methods, the object prior is implicitly expressed by assuming that the object
is contained in every image. In addition to these unsupervised
or weakly supervised segmentation methods, the learning based
method segments the specific object by first learning the object
prior in a supervised manner and then applying the prior on the
new image to conduct the specific object extraction [16]–[21].
Compared with the unsupervised manner, the learning based
method can learn more accurate object prior and result in better
segmentation results. This paper focuses on learning based specific object segmentation method.
Two problems are usually considered in the learning based
specific object segmentation method. One is how to exactly
construct the object prior from the training data. Considering
there are many object variations among the objects, such as thechanges of rotation, scale, translation, pose and shape, the generated object prior ought to be robust to these variations. The
other problem is how to successfully detect and segment the object from the complex backgrounds. This is challenging due tothe variations of the objects and the complexities of the backgrounds. Some examples can be found in Fig. 1, where there aremany changes among the objects in Fuwa, such as the rotation,
the scale and the poses. Meanwhile, the backgrounds may becomplex, such as the street scene in FedexTrucks.
It is also observed from Fig. 1 that the logo usually stays
fixed although the objects change significantly. The reason
is that the logo is the identity of a brand and should be
fixed to identify the referring commodity. Since the logos

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Fig. 2. The matching results by SIFT features. Six classes are shown. For each class, a pair of images randomly selected from the image group is shown.

usually share similar local texture, they can be easily matchedby the existing local region matching method. Hence, logo
extraction is easier than the object level based segmentation.
Some of the matching examples are shown in Fig. 2, where
the results of the six image pairs are displayed. SIFT features
are used for the matching. We can see that the logos are
successfully matched from the image pairs, while there aremany matching mistakes on the object. Note that the matchedlogos can provide much information of the objects, such as
the location and the local appearance of the objects, which
can simplify the object extraction. Furthermore, the boundaryof the objects can be located through learning the relationshipbetween the logo and the object, which results in a new shapemodel and more accurate object segmentation.
By extending logo to be a more general concept such as
the similar local region, the logo based segmentation method
can be applied to many specific object extraction tasks, suchas detecting and segmenting objects with specific characteristics from complex backgrounds or tracking a specific object
from the video based on fixed local features. Besides apparent
applications in image and video editing, the proposed method
also can be used for several potential applications, such as
content-based image retrieval [42].
In this paper, we propose a logo based object segmentation
method, which consists of three steps (first reported in [22]). The
first step is to detect the logos from the original images by logo
matching. SIFT feature is used for the matching. In the second
step, we extract the object boundary based on the logo and theobject shape prior. Considering the changes between the object
and the object shape prior, we take the shape variations into account. For each image, we vary the shape model by affine transform and search the transformed shape model that best fits the
local edge of the image as the object boundary. In the third step,
we use the regions inside and outside the boundary to form theunary term of the Markov Random Field (MRF) based energyfunction. The objects are finally segmented by the graph-cuts algorithm. We collect the LogoSeg datasets from the web to verify
the proposed method. MOMI dataset is also used for the verification. The experimental results demonstrate the effectivenessof the proposed method.
The rest of the paper is organized as follows. The related work
is introduced in Section II. In Section III, we present our proposed logo based object segmentation method. Experimental results are provided in Section IV to validate the efficiency of the
proposed model. We discuss the proposed method in Section V.
Finally, Section VI gives the conclusion of this paper.

II. RELATED WORKIn the semantic object segmentation, an accurate object prioris required to distinguish the object from the backgrounds. In theexisting methods, the object prior is usually generated by two
methods: supervised method and unsupervised (weakly supervised) method. Supervised method learns the object prior fromthe training data. Compared with the second one, supervised
method usually achieves better segmentation, since the object
prior can be more accurately learned by the supervised manner.
Many supervised methods have been proposed, such as the interaction based method [23]–[26] and training based method[16]–[21]. The interaction based method generates object priorthrough the location and appearance of the object labeled by theuser, such as Grabcuts [25] and random walker based segmentation method [26]. However, the interaction based method is notsuitable for the realistic applications with a large number of images. Compared with the interaction based method, the training
based method can be used in more applications.
The training based method generates the appearance prior[27], [28] or the shape prior [16]–[20] of the object based on the
training data. In this paper, we focus on the shape prior basedmodel. Gabor filtering feedback was employed to describe
local edge information by Wu et al. [16], which searched thestructure of the Gabor filter feedback shared by the training
images to form the common template. For a new image, thesimilar structure of the Gabor filter feedbacks was searched toobtain the common object. In the work of Ferrari et al. [17], thecommon template was obtained through finding, assemblingand refining similar K adjacent segments (kAS) from original
images. By using Hough-style voting and non-rigid point
matching algorithm, similar objects can be detected from the
new images. In the work of Jiang et al. [18], a common object
detection method that was robust to object variations such astranslation, rotation and scale was proposed. Thin plate spline(TPS) parameterizations were used to learn the mean shapefrom the annotated images, and the extended TPS-PRM algorithm was used for similar contours matching in new images.
In the work of Ma et al. [19], contours were represented byfragments of edges. The common template was generated byaffinity propagation clustering algorithm. Similar object wasthen detected from new images by maximal clique computationof the corresponding weighted graph. Region-based descriptor
rather than edge-based descriptor was used to describe local
area by Bagon et al. [20] which employed a self-similarity
descriptor [21] to describe local region. The specific objects
were extracted from the training images base on the descriptor
matching. The common template was generated by averaging

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the obtained descriptors. For new images, objects were segmented through searching the region that was most similar tothe object template.
There is also much research on imposing prior shape knowledge into image segmentation [29]–[35]. Cremers et al. in [29]added a variational integration of nonlinear shape statistics intoa Mumford-Shah based segmentation process to guide curve
evolution, which can segment objects with misleading information due to noise, clutter and occlusion. In [30], Cremers
et al. measured the dissimilarity between two level sets based
shape priors by computing the area of the set symmetric difference. Based on the invariant shape dissimilarity measurement,a statistical shape prior that can encode multiple fairly distinct
shapes was introduced into level set based segmentation for accurate object segmentation. In the work of Schoenemann et al.
[31], the shape prior was represented in the product space referring to the shape prior and images. The objects similar tothe shape prior can be searched on the product space (represented by graph) by Minimum Ratio Cycle algorithm. Klodt etal. in [32] represented the shape prior in terms of moment constraints and added the shape prior into a convex framework forimage segmentation. Several shape based moment constraints,
such as area constraint and centroid constraint, were formulated
as nested convex sets, which resulted in the simple minimization. Veksler et al. [33] proposed a generic shape prior named
the star shape prior, which was not specific to any particular
object, but rather applied to a wide class of objects, in particular to convex objects. The shape prior was then added into
MRF based segmentation method for accurate segmentation.
In [34], Lempitsky et al. added bounding box shape prior into
interactive based graph cut segmentation to improve boundingboxes based segmentation. The shape prior was formulated as
the tightness between the object boundary and bounding box,
which can make the segment close to the bounding box. Das etal. [35] represented shape prior by compact shape, which usedthe directions of the connected boundary pixels to measure thesemantic of current shape. The shape prior was added into MRF
segmentation and can be minimized by graph cuts algorithm.
In addition to the supervised segmentation methods, unsupervised (weakly supervised) segmentation methods automaticallygenerate object prior (or require less user interaction). Several
unsupervised (weakly supervised) segmentation methods havebeen proposed, such as co-segmentation [6]–[12], [36]–[40] andweakly supervised semantic segmentation [13]–[15].
The co-segmentation extracts the specific object by segmenting common object from a group of images [41]. By
introducing an additional foreground similarity constraint into
single image segmentation, the object can be automaticallysegmented from the images. Several co-segmentation methods[6]–[12], [36]–[39], [42] have been proposed in the past few
years.
MRF based co-segmentation method adds the constraint offoreground similarity into traditional MRF based segmentation
model for common object segmentation [41]. The key is how
to measure foreground similarity. Meanwhile, since the foreground similarity measurement makes the energy minimizationdifficult, another problem is how to minimize the energy. In theexisting MRF based co-segmentation methods, the similarity

IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 8, DECEMBER 2013

measurements such as L1-norm [6], L2-norm [7], reward
model [8], and Boykov-Jolly model [9], [43] have been used.
The corresponding optimization methods, i.e., trust region
graph cuts (TRGC) method [6], quadratic pseudo boolean
optimization method [7], maximum flow procedure of graph[8], and dual decomposition [9] were also proposed for theminimization [40]. Instead of introducing foreground similarity
into MRF model, Chu et al. in [42] proposed a MRF based
co-segmentation method from the perspective of common
pattern discovery. In the method, the foreground similarities
were represented as feature matching and were discovered bycommon pattern discovery (using SIFT feature matching anddensity-based clustering). Then, based on the confidence maps
obtained from the common pattern discovery, the unary term of
MRF based segmentation was designed. Finally, the Graph-cuts
algorithm was used to minimize the model for common object
segmentation. Several logos have been tested to be successfully
segmented by the method in [42]. Note that the logos in [42]are treated as common objects, and the purpose of [42] is tosegment common logos from the image group. However, in our
method, logos are treated as local information, which are used
to locate the object and provide the appearances of the object.
Furthermore, we also consider the shape variations among theobjects for accurate object segmentation.
Apart from MRF based co-segmentation method, discriminative clustering and spectral clustering method were combined
to segment common objects by Joulin et al. in [11]. The classifier by spectral clustering technique was treated as segmentation. The classifier that best discriminates the foregrounds andbackgrounds was searched as the final co-segmentation classifier. An interactive co-segmentation method was proposed in thework of Batra et al. [43] which used an automatic recommendation system to achieve accurate common object segmentation.
In the work of Vicente et al. [44], an object co-segmentation
method was proposed. The authors first segmented the images
into a set of overlapping local regions. Then, the co-segmentation was casted as MRF setting problem which was optimized
by Loop Belief Propagation. Mukherjee et al. in [45] presented
a scale invariant co-segmentation method which was robust to
the scale variation. The fact that the matrix comprised of thecommon objects should have a rank of one was used to search
the common objects. Chang et al. [46] proposed a novel global
energy term that made the energy function submodular. Hence,
Graphcut algorithm can be used to efficiently solve the optimization problem. Furthermore, the co-saliency map was employed to construct the unary term of the energy function. Kim
et al. [12] segmented the common objects by diffusing the heatamong the different images according to the region similarities.
To learn the object prior, several weakly supervised learning
methods [13]–[15] focus on simultaneously learning the appearance model and labeling the pixels. For example, Vezhnevets et al. in [13] proposed a graphical model, i.e., multiple
image model (MIM), to recover the pixel labels of the weaklylabeled training images. The segmentation was achieved by applying the Alpha Expansion algorithm to find the approximate
MAP state of MIM. In [14], a conditional random filed based
weakly supervised learning method was proposed. In the model,
the consistency between the segmentation and training samples

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TABLE IVARIABLES DESCRIPTION

Fig. 3. The flowchart of the proposed method.

Fig. 4. An example to illustrate the logo detection.

was considered. In [15], the object model was learned from theweakly labeled training images by active learning, which alternatively performed the weakly supervised learning and pixels’label for accurate learning. Because of the complexity of the
background and the variations among the objects, there is stilla substantial gap between the supervised methods and this typeof weakly supervised methods.

of matching pixels inside the windows
matching pixels .

(1)
The other is the area of the window
the score of window
is given by

A. Logo Detection
To detect the logo, we give a training image for each logo
, whereis the number of logos. To explicitly infer the variables used in this paper, we show these variables in Table I. For a new image
where denotes the -th
image in the -th image set related to logo , we detect thelogo by three steps. In the first step, image
is matchedwith the training image
by SIFT matching. The matchingpixelsinrelated to the logo pixels
in(as shown in Fig. 4(a)) are selected. In the second step,
we consider the detection result as one of the initial windows
setting by sliding windows method. Eachinitial window is scored by window evaluation and the best
window is selected as the final result. One example is shownin Fig. 4.
Two aspects are considered to score the window. One is thematching ratio
which is defined as the ratio of the number. Based on

and

,

(2)

III. THE PROPOSED METHOD
The flowchart of the proposed method is shown in Fig. 3,
where there are three steps, i.e., the logo detection, the object
detection and object segmentation. In this section, we introduce
these steps.

to the total number ofwhere
is scaling factor. We can see that the windows
covering most of the matching pixels will have large scores.
Meanwhile, for a certain set of matching pixels (such as thematching pixels inin Fig. 4(b)), there are many windows(such asandin Fig. 4(b)) that cover these matchingpixels and have the same value of. Hence, the second
term, i.e., area term, is introduced to order these windows. We
believe that a small window is more compact and reasonable
than a large window, such as the window
compared within Fig. 4(b). Furthermore, only using the matching ratio
will lead to the selection of the window covering all of thematching pixels, such as. Because there may be several
wrong matching pixels that are far away from the logo, covering
these wrong pixels results in detection of a large unsuccessful
window. Hence, we use the area term to avoid these wrong
detections. It is seen that deleting these wrong matching pixels
results in a small decrease of matching ratio but dramaticallyincreases the area term, and finally results in a large score.
B. Object Segmentation
1) Segmentation Model: We segment the object by Markov
Random Filed segmentation method, which searches label of2190

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Fig. 6. The variations of the shape model.

Fig. 5. The shape model used in the proposed method. The origin (0, 0) of theis the location of the logo.
planar coordinate system

image pixels that minimize the energy function represented as
(3)
where
is the label of pixel . is the set of pixels.
The foreground has label 1, and 0 for the background.
is the unary term which describes the probability of labelingto . The foreground and background models are represented
as the Gaussian mixture model with parametersand, respectively. To segment the specific objects of the
proposed method, the foreground model and background model
are required. Hence, we need to estimate the parametersandof the foreground model and background model.
In the second term of (3), is the set of neighboring pixels.
Tow pixels are neighbors if they satisfy 3 3 neighbor relationship.
denotes the pixels’ consistency between two
neighboring pixels, which is usually defined as the distance between the appearances of the pixel pair. The second term makes
the labels of neighbor pixels consistency. Based on (3), the labelwith the minimum energy function is searched by the graph-cuts
algorithm.
To segment the specific object, we require to obtain the appearance models of both foreground and background referringtoand. In our model, we estimate these parameters by searching the object boundary, where the regions insideand outside the boundary are used to generate the foregroundappearance model and the background appearance model.
2) Boundary Extraction Based on the Logo: In our method,
the detected logo simplifies the boundary extraction by two
aspects. Firstly, the logo provides the roughly location of
the objects. Secondly, the logo based object shape prior caneasily handle the changes of the object and result in successful
boundary extraction.
a) Logo Based Object Prior: The shape of the specificobject extracted from any image is used as the object prior. We
also consider the location of the logo
locating in the object
shape. Hence, the object prior can be represented as.
In the object prior, shape is represented in a planar coordinate
system. The location of the logo
is treated as theofplane, and the shape boundary locates around the, as shown in Fig. 5. The relationship between
andthe shape is represented by the location of the boundary pixels.

b) The Model Prior Variations: Based on the logo based
shape prior, the object is extracted by two steps. In the first step,
we treat the image as aplanar coordinate system, and set
the center of the detected window
as the origin
in the planar coordinate system. The shape model is then put
into the image based
planar coordinate system by aligning
with. Assuming there are no variations among
the objects, the shape boundary is then considered as the object
boundary.
Since there are variations among the objects, such as thechanges in rotation, scale and pose, the initial object boundary
usually does not locate along the object boundary. Hence, inthe second step, we vary the to cope with the shape changes,
which can be represented as(4)
where is the parameter of the variations, is the variation operator, is the new shape. By considering the shape changes, we
obtain the object boundary by searching a variation of the shapethat best fits the local edge of the image, which is represented
as
(5)
is
where is considered here since refers to .
the measurement of the fitness between the varied shapeand the local edge of the image . By obtaining the transform parameter , the final object boundary is obtained through varying
with the by (4), and then aligning with the logo location.
c) The and for Shape Variations: We consider the
shape changes by affine transform which contains the usual
shape variations, such as the variations of rotation, scale andtranslation. For a pixel with location, the new location
after affine transform can be obtained by
(6)
where
is a parameter. The original shape
can be varied by adjusting different . Fig. 6 shows several
shape variations by selecting different . Note that we also givethe logo location(red node in each image) after the affine
transform.
d) The measurement : To measure the fitness between
shape derived from and image , we first extract the binaryedge map of the image by edge detection method. In , theedge pixels have value 1, and 0 for the background pixels. One

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Fig. 7. (a): The binary edge map of the original image. (b): The shape model.
(c): Example to calculate the fitness of the shape with an image edge map.

example is shown in 7(a). We denote the set of edge pixels as .
Then, we introduce the shape into to obtainby aligning
the logoin with the logo
in , as shown in
Fig. 7(c). The set of pixels on the shape boundary is denoted as. We can see that only the pixels on the image edge and theshape boundary are considered for measurement.
In the fitness evaluation, we only consider the edge pixel
around the shape boundary in . For any pixel, wedefine the distance
between the pixel and the image
edge as the smallest geometric distance between theand the pixels, i.e.,

parameters may lead to an undesirable local minimal value. In
the proposed method, before applying the Nelder-Mead simplex
method, we perform a rough search in the 6-dimensional parameter space, such as working on a coarse to fine set of grids.
Since the dimension is still large for practice performance, we
use a separation performance to divide the affine transform into
5 sub-transforms and lead to two dimension search space.
Four sub-operators are considered. They are rotation, scale,squeezing and translation, respectively. To simplify the solution, the scale is also divided into two sub-operators. One is thescale that has same scale factors on the width and the height.
The other has different scale factors on the width and the height.
Hence, there are five sub-transforms. The transform matrixes ofthese sub-transforms are expressed as

(12)

(7)
where

is defined as(8)andare locations of pixel and , respecand
tively. One example is shown in Fig. 7(c), where the distance
between and image edge (white contour) is the geometric distance
between and . The has the smallest distanceto among the pixels of the image edge.
Based on in (7), we evaluate each pixelby(9)
where is variable referring to the width of the band around theshape contour. Based on (9), the fitness of shape to image isdefined as(10)
We can see from (10) that the fitness is the sum of distances
between all shape pixels to the image edge. When fits the local
edge of the image, these shape pixels have small distances to theimage edge and result in large. Thus, a small fitness value
is obtained. Otherwise, a large value is given to the shape when
the shape does not well fit the local edge of the image.
e) The Optimization: Based on (10), we can rewrite (5) by(11)
The object boundary can be extracted by searching that satisfies (11). To search , we use gradient free based minimization method, such as Nelder-Mead simplex method. However,minimizing the cost function with respect to six parameters is
a complicated computational task, since directly searching thewhere the transform matrixes of rotation, scale ( andare the first scale and second scale transform, respectively),squeezingare shown.
is the angle in the rotation.
are parameters related to the scale and squeezing
transform. For the translation transform, we simply change
the logo location
with distanceandon the row
and column, i.e.,. By consideringas thetranslation transform matrix, the original transform matrix
can be represented as:(13)
and the parameterchanges fromto.
Although the number of parameters increases, the advantageof the sub-transforms is that the objects usually have largechanges on two transformations, i.e., the rotationand scale. For the others, the objects have small changes. Hence,
we can perform the rough search by only considering thetransformations of rotationand scale . Note that it is a
two dimensional search space that can be quickly achieved.
Based on the rough search, we then obtain the more accurate
shape by minimizing (11) using Nelder-Mead simplex method.
After obtaining , the object boundary is obtained by transforming the original shape with respect toby (4).
3) Object Segmentation: After extracting the object
boundary, the images are classified into two regions, the region
inside the boundary and the region
outside theboundary. We consider
andas the foreground region
and background region respectively, which are used to estimate
the parametersandfor the foreground modeland background model as introduced in Section III-B1. Thenthe foreground model and background model are introduced
into the unary term of the segmentation energy function in (3).
Finally, the energy function is minimized by the graph-cuts
algorithm and the object is extracted through the obtained label.

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Fig. 8. Images in the logo dataset.

Fig. 9. The logo detection results. The logos are shown as the red points in the images.

IV. EXPERIMENTAL RESULTS
In this section, we verify the performance of the proposed
method. A Logo dataset is collected from web to verify the proposed method. We also use the MOMI dataset given by [42] forverification. Some subjective and objective assessments of segmentation results are reported.
A. Parameters Setting
We first introduce the parameter setting in our experiments. In
the logo detection, the training image with simple background isselected as logo sample. The source code of the SIFT matchingreleased by the author is used.1 The initial window is set throughthe sliding windows method. In (2),. In the object
segmentation, the number of Gaussian distribution in Gaussian
mixture model is 5 for both foreground and background model.
In the object detection, we use the method in [5] to obtain theedge map by setting. For the in (9), we set
forthe rough search and the minimization. In the rough search, theis searched in range ofwith step 0.2 and is searched
in range ofwith step 0.2.
B. Results of Our Proposed Method
In order to completely verify the proposed method, we collect images from the web such as Flickr and Google to form
LogoSeg dataset. In the LogoSeg dataset, there are 13 classes,
such as Adidas, Cocacola, Fedextrucks and Iphone. Some of theimages are shown in Fig. 8. It is seen that the objects have many
variations among the objects, such as the color changes of theobjects in Cococola. Meanwhile, many images have complex
1http://www.cs.ubc.ca/

lowe/keypoints/

Fig. 10. The results of curve extraction by the proposed method. (a)(c)(e): The
extracted boundaries by the rough search. (b)(d)(f): The final extracted object
boundaries.

backgrounds, such as street scenes in Fedextrucks. The variations among the objects and the complexities of the backgroundmake the object extraction challenging.
We also verify the proposed method on MOMI dataset given
in [42]. In the MOMI dataset, there are 12 classes, each of whichcontains three to eight images. In each class, the common pattern logos or regions are contained among the images. In our
experiments, all the classes (12 classes) in MOMI dataset areused for complete verification. The common logos or regions aretreated as the logos in our model. Because there are no training
logos in MOMI dataset, we collect the similar logos and targets
from the web to form training logos and object shape models.
The ground-truth given by [42] are used for verification.
We first show the logo detection results by the proposed
method. The results are shown in Fig. 9, where the logo isdenoted as the red dot in each image. We can see from Fig. 9

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Fig. 11. The segmentation results of LogoSeg dataset by the proposed method. The original images are shown in the row 1, 3 and 5. The corresponding results
are shown in the rows 2, 4 and 6, respectively.

Fig. 12. The segmentation results of MOMI dataset by the proposed method. The original images are shown in the row 1, 3 and 5. The corresponding results areshown in the rows 2, 4 and 6, respectively.

that the logos are successfully detected from these images. The
successful location of the logo is caused by the stable local
textures shared by the logos. These stable local textures provide
the stable logo location to benefit the object extraction of thefollowing steps.
The extracted boundaries of the objects are shown in Fig. 10,
where Fig. 10(a)(c)(e) show the extracted boundaries by
the rough search. The final object boundaries are shown in
Fig. 10(b)(d)(f). We can see that the rough search can provide an
object boundary near the objects in these images. Meanwhile,the final searching can obtain more accurate object boundaryfrom the background noises.
The segmentation results of the proposed method are shownin Figs. 11 and 12, where the segmentation results of the LogoSeg dataset and MOMI dataset are shown, respectively. In
Fig. 11, the test images and the corresponding segmentation results of six classes in LogoSeg dataset are displayed. The original images are shown in the rows 1, 3 and 5. The rows 2, 4 and6 display the corresponding segmentation results. It is seen from
Fig. 11 that the proposed method successfully segments the objects from these images with complex backgrounds, such as thesegments in Fuwa. The segmentation results of MOMI dataset
are shown in Fig. 12, where the results of eight classes are represented. For each class, we display the results of three images.
It is seen that the proposed method can also segment the objects
from these images, such as “Pisa” in class Pisa.

C. Objective Evaluation
To evaluate the performance of the proposed method, we
compare the proposed method with several existing related
methods such as the methods in [16], [12], [11] and [42]. The
method in [16] is a shape based object extraction method,which learns the shape model from the training images by
discovering the similar local edge structures. Gabor filter isused to describe the local edge and the edge structure. In our
experiments, the source code released by the authors is used.2
The authors in [12] propose an anisotropic heat diffusion based
co-segmentation, which first locates the similar regions among
the images by clustering method. The location is then used as
the seed to segment the object by anisotropic heat diffusion
method. The code3 released by the author is used in our experiment. To improve the performance of the method in [12],we also vary the parameters such as the segment number andGaussian weight for better performance. The authors in [11]propose a co-segmentation method by combing discriminate
clustering method and spectral clustering technique. The classifier that best discriminates the foregrounds and backgrounds
was searched to achieve common object segmentation. We
perform the method in [11] using the source code released by2http://www.stat.ucla.edu/ywu/AB/ABbasicDec292010.zip
3http://www.cs.cmu.edu/

gunhee2194

IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 8, DECEMBER 2013

TABLE IICOMPARISON BETWEEN THE EXISTING METHODS WITH THE PROPOSED METHOD IN TERMS OF F-MEASURE

the authors.4 In the experiments, Chi-square kernel is used.
SIFT features are selected for local regions representation.
In the method [42], common pattern discovery algorithm isfirst performed to obtain the confidence maps representing
the potential of pixels belonging to common patterns. Then,the MRF based energy is calculated based on the confidence
maps for common object segmentation. In our experiments,
we implement the method in [42] by Matlab code. Level-set
method is used for confidence map generation. The parametersof the density-based algorithm are adjusted for better results.
In our experiments, F-measure is used for objective evaluation. F-measure is defined as, whereis the ratio of the number of successful segmented
foreground pixels to the number of segmented foregroundpixels, andis the ratio of the number of successful
segmented foreground pixels to the number of foregroundpixels in Ground-truth. A large F-measure refers to an accuratesegmentation. Note that the results of the method in [16] arecontours rather than regions. We calculate the F-measure of
the method in [16] by replacing the region and ground truth
to the rectangles exactly covering the region and the groundtruth respectively. Meanwhile, there are more than two images
in each class. We use the mean F-measure over all images toevaluate the performance of each class. Table II concludes theF-measures of the methods in [16], [12], [11], [42] and the
proposed method.
From Table II, we can see that the method in [16] achieves
good performance on some image groups, such as Adidas andPringles. Meanwhile, there are unsuccessful extractions, suchas the results of Heincken and Nike. The reason for the unsuccessful extraction is caused by the shape variations among
the objects. For the method in [12], the objects are segmented
4www.di.ens.fr/

joulinfrom the images such as Wang and Sulley. Furthermore, there
are unsuccessful extractions, such Iphone and Starbucks. The
unsuccessful segmentation is caused by the reason that themethod in [12] focuses on the common objects segmentation
containing similar colors, which is not suitable for the common
objects segmentation containing similar contours. The method
in [11] achieves successful objects segmentation in several
classes, such as Adidas and Pisa. Unsuccessful segmentationis also obtained by the other classes, such as Iphone. The
unsuccessful segmentation is caused by the pose variations
among the objects. For the method in [42], the objects can be
successful extracted on several datasets, such as Adidas and
USAFlag. Unsuccessful segments are also obtained, such as
Fedextruck and Redbull. The unsuccessful segmentation iscaused by the fact that extracting common objects by common
pattern discovery can be confused by the complex backgrounds.
Compared with the existing methods, we can see that the proposed method achieves the largest F-measures in many classes.
The improvements are mainly caused by two reasons. One isthat logo detection simplifies the object detection. The other isthat the shape transform is considered in the proposed method
to overcome the shape changes among the objects.
V. DISCUSSION
In this paper, we use classical SIFT feature for logo detection.
Note that other matching methods can also be used for the logo
matching. We also test the proposed method based on the othermatching methods to verify the proposed method. The F-measures of the corresponding results are shown in Table III, where
the results based on SIFT matching and SURF [47] matching areshown. We can see that the SURF based method achieves larger
F-measures on several classes compared with SIFT matching,such as Iphone and Starbucks. But for some classes, the lower

MENG et al.: FROM LOGO TO OBJECT SEGMENTATION

2195

TABLE IIITHE RESULTS BY DIFFERENT MATCHING METHODS. SIFT MATCHING AND SURF MATCHING ARE CONSIDERED

ting methods such as random search [48] or manual search [49]can also be used for optimization.
VI. CONCLUSION

Fig. 13. Failure cases by the proposed method. The proposed method fails forthese classes due to cluttered background ((a) and (b)) and the similarity between
the object and background ((c)).

F-measure values are obtained by SURF based method, such as
USAFlag and Sulley. Considering all classes, the mean F-measure value of SURF based method (0.6338) is very close to themean F-measure of SIFT based method (0.6328), which demonstrates that both SIFT and SURF can be used as logo detectionin our method.
In addition, it should be noticed that false segmentation will
be caused when the image contains significantly cluttered background or the object is depicted in very similar backgrounds. Examples can be found in Fig. 13(a) to (c), where some false segments can be observed. It is seen that the cluttered backgrounds
can confuse the logo detection and then result in unsuccessful
object segmentation, such as Fig. 13(a) and (b). Meanwhile, thesimilarity between the background and foreground increases theambiguity of the segmentation task and leads to incomplete segmentation, such as Fig. 13(c).
In the proposed method, the gradient free based minimization
method is used to minimize the model. In the minimization, aprecise initial value is required for the accurate search of parameters. Because we can divide the affine transform used in
the model into 5 sub-transforms and finally lead to two dimension search space, we use a rough search working on a coarse
to fine set of grids to quickly obtain the initial value. When extending the method to the model with more complex parameter spaces, the large number of parameters will result in a large
search space which leads to large computational cost of the gridsearch method. Note that for non-real-time applications, the gridsearch based method can also be used for initial value setting.
For real-time applications, other approximate initial value set-This paper proposes a specific object segmentation method
based logo detection. In the method, the logo is firstly detectedusing SIFT matching. Then the objects are segmented basedon the location of the logo and the shape model. To cope withthe shape variations, affine transform of the shape model isconsidered. The best shape variation is searched by the NelderMead simplex method with a simple initial rough search. We
collect many images from the web to test the method. The experimental results demonstrate the effectiveness of the proposed
method.
ACKNOWLEDGMENT
The authors would like to acknowledge Wen-Sheng Chu for
his kind advice in the MOMI cosegmentation implementation.
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Fanman Meng received the B.Sc. degree in computer science and technology and the M.Sc. degree
in computer software and theory in 2006 and 2009
respectively. Since September 2009, he has beenworking toward the Ph.D. degree in the intelligent
visual information processing and communicationlaboratory (IVIPC) at University of Electronic
Science and Technology of China (UESTC). His
research interests include image segmentation,
object detection and visual attention.

MENG et al.: FROM LOGO TO OBJECT SEGMENTATION

Hongliang Li (SM’12) received his Ph.D. degree inelectronics and information engineering from Xi’an
Jiaotong University, China, in 2005. From 2005 to
2006, he joined the visual signal processing and communication laboratory (VSPC) of the Chinese University of Hong Kong (CUHK) as a Research Associate. From 2006 to 2008, he was a Postdoctoral
Fellow at the same laboratory in CUHK. He is currently a Professor in the School of Electronic Engineering, University of Electronic Science and Technology of China. His research interests include image
segmentation, object detection, image and video coding, visual attention, andmultimedia communication system.
Dr. Li has authored or co-authored numerous technical articles in well-known
international journals and conferences. He is a co-editor of a Springer book titled “Video segmentation and its applications”. Dr. Li was involved in many
professional activities. He is a member of the Editorial Board of the Journal on
Visual Communications and Image Representation. He served as TPC members
in a number of international conferences, e.g., ICME 2013, ICME 2012, ISCAS
2013, PCM 2007, PCM 2009, and VCIP 2010, and served as Technical Program
co-chair in ISPACS 2009, and general co-chair of the 2010 International Symposium on Intelligent Signal Processing and Communication Systems. He willserve as a local chair of the 2014 IEEE International Conference on Multimedia
and Expo (ICME). Dr. Li was selected as the New Century Excellent Talents in
University, Chinese Ministry of Education, China, in 2008.

Guanghui Liu received the M.Sc. and Ph.D. degrees
in electronic engineering from University of Electronic Science and Technology of China (UESTC),
Chengdu, Sichuan, China, in 2002 and 2005 respectively. In 2005, he joined Samsung Electronics,
South Korea, as a senior engineer. Since 2009, hehas been with the School of Electronics Engineering,
UESTC, as an associate professor. His general
research interests include digital signal processing
and telecommunications, with emphasis on digital
video transmission, and OFDM techniques. In theseareas, he has published tens of papers in refereed journals or conferences, andreceived more than 10 patents (4 U.S. granted patents). Dr. Liu served as the
publication chair of the 2010 International Symposium on Intelligent Signal
Processing and Communication Systems (ISPACS 2010).

2197

King Ngi Ngan (F’00) received the Ph.D. degree inelectrical engineering from the Loughborough University in U.K. He is currently a chair professor at
the Department of Electronic Engineering, Chinese
University of Hong Kong. He was previously a full
professor at the Nanyang Technological University,
Singapore, and the University of Western Australia,
Australia. He holds honorary and visiting professorships of numerous universities in China, Australia
and South East Asia.
Prof. Ngan served as an associate editor of IEEE
Transactions on Circuits and Systems for Video Technology, Journal on Visual
Communications and Image Representation, EURASIP Journal of Signal Processing: Image Communication, and Journal of Applied Signal Processing. He
chaired and co-chaired a number of prestigious international conferences on
image and video processing including the 2010 IEEE International Conference
on Image Processing, and served on the advisory and technical committees ofnumerous professional organizations. He has published extensively including
3 authored books, 6 edited volumes, over 300 refereed technical papers, andedited 9 special issues in journals. In addition, he holds 10 patents in the areasof image/video coding and communications.
Prof. Ngan is a Fellow of IET (U.K.) and IEAust (Australia), and an IEEE
Distinguished Lecturer in 2006–2007.