Rapid Mobile Object Recognition
Using Fisher Vector
Yoshiyuki Kawano and Keiji Yanai
The University of Electro-Communications,Tokyo
1-5-1 Chofugaoka, Chofu-shi, Tokyo, 182-8585 Japan
Email: kawano-y@mm.inf.uec.ac.jp, yanai@cs.uec.ac.jp
Abstract—We propose a real-time object recognition method
for a smartphone, which consists of light-weight local features,
Fisher Vector and linear SVM. As light local descriptors, we
adopt a HOG Patch descriptor and a Color Patch descriptor,
and sample them from an image densely. Then we encode themwith Fisher Vector representation, which can save the numberof visual words greatly. As a classifier, we use a liner SVM thecomputational cost of which is very low. In the experiments, wehave achieved the 79.2% classification rate for the top 5 category
candidates for a 100-category food dataset. It outperformed theresults using a conventional bag-of-features representation witha chi-square-RBF-kernel-based SVM. Moreover, the processing
time of food recognition takes only 0.065 seconds, which is fourtimes as faster as the existing work.

I.

I NTRODUCTION

In recent years, smartphones such as iPhone and Android
phones have become popular and obtained more computational
power. Then object recognition on smartphones in a realtime way becomes possible. Currently, a quad-core CPU iscommon as a smartphone’s CPU. Old-style mobile image
recognition systems carry out image recognition processing
on the server side with much computational resource. They
require wireless connection, and the communication delay isunavoidable. With the server-side recognition, real-time object
recognition is impossible. In addition, when the number ofusers increases, response time will be much degraded unlessmaking computational resource increased.
On the other hand, client-side processing on object recognition on a common smartphone needs no wireless connection
and no commutation delay. This is one of the requirements formobile real-time object recognition. However, computational
resource is relatively limited compared to server-side processing where many CPUs are available. To realize real-time object
recognition in the client side on a common smartphone, amethod which enables us to use its computational resource
efficiently is needed. This is apparently a non-trivial task.
Recently, several feature image representations which aresuitable for a faster linear classifier have been proposed. Past
style image recognition is common to classify images with
SVMs with non-linear kernels. For N training samples andthe number of support vectors M , computational complexityof non-linear SVM is O(N 2 ) ∼ O(N 3 ) in training, and theprocessing time is proportional to M in testing, which are very
high cost and requires large memory space. On the other hand,the computational costs of a linear SVM is O(1), which means
one-time evaluation of a linear SVM requires one computationof an inner product, while its performance is lower that thatof a SVM with a non-linear kernel.

To resolve this, some image representations are proposed
recently, which can boost recognition performance in caseof using a linear SVM. Especially, Fisher Vector [1], [2] is
known as a high performance method among the recent image
representations. it is suitable for a linear classifier, and wasturned out that it can improved recognition accuracy more than
popular combination of bag-of-features (BoF) and a non-linear
SVM. Moreover, while BoF needs larger dictionary to improve recognition accuracy, larger dictionary brings increase ofcomputational cost for searching nearest visual words. On theother hand, Fisher Vector is able to achieve high recognition
accuracy with even small dictionary, and low computational
complexity. This is an advantage for mobile devices.
Thus adopting Fisher Vector as encoding method is betterin terms of recognition accuracy and processing time for amobile object recognition. But a system on a smartphone does
not exist so far that carries out rapid and high precision image
recognition with Fisher Vector. Then we propose a recognition
method for a smartphone which is rapid and accurate bymaking good use of computational resource of the smartphonewith Fisher Vector.
In the experiments, we have achieved 79.2% classification rate within the top five candidates for a 100-category
food dataset with ground-truth bounding boxes. It is better
than the exist food recognition method [3] by 11.0%, andprocessing time of 100 kinds of image recognition takes
only 0.065 second. It is four times as faster as the existing
method [3]. Moreover, we achieved the better results thanthe exist food recognition method running on the serverside regarding classification accuracy on 100 food categories.
Therefore, the proposed method is more efficient than existing
image recognition methods for a smartphone.
The rest of this paper is organized as follows: Section
II describes related work. In Section III, we explain the
proposed method for rapid and accurate image recognition ona smartphone. In Section IV describes the experimental results,
and in Section V we conclude this paper.
II.

R ELATED W ORK

In this section, we introduce some related works in termsof object recognition on a smartphone.
As commercial services on image recognition for smartphones, Google Googles1 is widely well-known. Google
Googles is an Android-based application which can recognize
logos, famous art, and famous landmarks and so on in photos
1 http://www.google.com/mobile/goggles/taken by users. However, recognition targets are limited to
specific objects the appearance of which stays unchanged,
which is different from this paper the target of which is genericobjects. Kumar et al. [4] proposed a system which recognizes 184 plant species using curvature based shape feature
peculiar leaf, after a user sends a leaf photo to server. These
two systems basically employs server-side image recognition,
while our goal is to implement a client-side generic object
recognition system.
Next, we explain image recognition methods on run asmartphone. Lee et al. [5]. proposed a mobile specific object recognition system on a smartphone, which recognizedregistered specific objects in a real-time way. They train atemplate, which is decomposed into descriptors of intensity
and gradient orientation, and employ matching method for realtime object detection and tracking. This is a mobile specificobject recognition system. Maruyama et al. [6] proposeda recipe recommendation system, which adopts only color
histogram based BoF and they are directly applied linear SVM.
Thus it was very poor accuracy for generic object appearancesof which are changeable. Kawano et al. [3] proposed amobile food recognition system, which adopts SURF based
BoF and color histogram applied χ2 kernel feature map withlinear SVM. SURF is popular robust local descriptor, but themethod was relatively high cost for rapid image recognition on
smartphone. And it was hard to recognize foods in real-time,
if the number of categories increases. Moreover, conventional
BoF is not suitable for rapid image recognition in terms ofrecognition accuracy and processing time.
In this paper, we propose a rapid and highly actuate image
recognition method for a smartphone. It adopts HOG Patchand Color patch as light local descriptors, and Fisher Vector
representation as encoding method. As a classifier, a linear
SVM which is very efficient is employed.
III.

P ROPOSED M ETHOD

In this paper, we implement rapid and high accuracy image
recognition system which can handle 100 kinds of categories
with Fisher Vector based feature. We adopt HOG Patch andColor Patch as local descriptor, and each local descriptors are
represented by Fisher Vector. Then they are classified withlinear SVM.
A. Image Feature
In this paper, we use the following local descriptors forFisher Vector encoding: HOG Patch and Color Patch.
1) HOG Patch: Histogram of Oriented Gradients(HOG)
was proposed by N.Dalal et al. [7]. It is similar to SIFT isterms of how to describe local patterns which is based on
gradient histogram. Since HOG description is very simple, it
is able to describe much faster than popular local descriptor
such as SIFT [8] and SURF [9]. This is important characteristic
to carry out real-time recognition on a smartphone. In addition,
it is able to extract local feature more densely. As a result, it
improves recognition accuracy.
We extract HOG features as local features. We divide a
local patch into 2times2 blocks (totally four blocks), andextract gradient histogram regarding eight orientations fromeach block. Totally, we extract 32-dim HOG Patch features.
Then the 32-dim HOG Patch is L2 normalized to an L2
unit length, not adopt HOG-specific normalization by sliding
fusion. PCA is applied to reduce dimensions from 32 to 24.
2) Color Patch: We use mean and variance of RGB valueof pixels as Color Patch feature. We divide a local patch into
2 × 2 blocks, and extract mean and variance of RGB value ofeach pixel within each block. Totally, we extract 24-dim Color
Patch features. PCA is applied without dimension reduction.
The dimension of a Color Patch feature are kept to 24-dim.
B. Fisher Vector
Fisher Vector [1], [2] can decrease quantization error than
BoF [10] by using of a high order statistic. Moreover Fisher
Vector is very highly efficient in recent encoding method [11],and almost higher rank team of recognition challenge used
Fisher Vector [12].
For the number of local descriptors T , set of local descriptors X = {xt , t = 1, ..., T } , Fisher Vector is defined asfollows.
KX
p(x|θ) =πi N (x|µi , Σi )(1)
i=1

where p(X|θ) is probability density function (pdf),∇θ log p(X|θ) is gradient of log likelihood, Fθ is the
Fisher information matrix, then Fθ is decomposed intoFθ−1 = L0θ Lθ .
0

−1 Y
Fisher Kernel K(X, Y ) = GX
θ Fθ Gθ is an inner productof Fisher Vector. Thus it is able to classify images efficiently
with a linear classifier.

According to [2], we encode local descriptors into Fisher
Vector. We choice probability density functions as Gaussian
mixture model (GMM). Then pdf is given as follows.
p(x|θ) =K
X

πi N (x|µi , Σi )

(2)

i=1

where x is a local descriptor, K is number of component ofGaussian, θ = {πi , µi , Σi , i = 1, ..., K} is a parameter ofGMM. πi is the mixing coefficient, µi is mean vector andΣi is the covariance matrix. At this point, we assume thatthe covariance matrix is diagonal and diagonal elements arepresented variance vector σ 2 .
The probability of xt is belong component i (estimatedposterior probability) is given as follows.
πi N (xt |µi , Σi )γt (i) = PN
j=1 πj N (xt |µj , Σj )(3)

Then the gradient with respect to the mean and variance isdefined as follows,Txt − µi
1 X
X
γt (i)(4)Gµ,i=√
T πi t=1
σi


T
X
1
(xt − µi )2X√
Gσ,i =(5)γt (i)−1σi2
T 2πi t=1

In this paper, the number of component of Gaussian is
32 and local descriptors reduced to 24 dimensions by PCA.
Thus each feature vector is 1536-dimensional. To improverecognition accuracy, we apply power normalization (α = 0.5)
and L2 normalization [2].

0.8
0.7

Classification Rate

X
X
Finally, gradient Gµ,iand Gσ,iare calculated for all theX
Gaussian. Fisher Vector Gθ is their concatenation. Therefore
Fisher Vector is 2KD-dimensional.

0.6
0.5
0.4

SURF-BoF(8 grid step)
HOG Patch-BoF(8 grid step)
HOG Patch-FV(8 grid step)
HOG Patch-FV
HOG Patch-FV (flip)

0.3
0.2
0.1

C. Classification0

As a classifier, we use a linear kernel SVM, and we adopt
the one-vs-rest strategy for multi-class classification. Linear
kernel is defined as the inner product of two vectors.
An object recognition method for a smartphone should
be low cost and low memory space. In case of linear SVM,
the inner product of a support vector and the weight of thecorresponding support vector are able to be computed inadvance. Then a linear SVM score is given by the inner productf (x) = hw, xi between input data vector x and weight vector
w. Therefore when N is the dimension of feature vector,
calculation of a SVM score requires O(N ) operations andO(N ) memory space. We train SVMs with LIBLINEAR [13]in off-line.
In the experiments, since the number of the given categories
was 100, we trained 100 linear SVM classifiers. We trained
linear SVMs for each feature. Then we combine the output
values of both linear SVMs for HoG features and Color
features in the late fusion manner
D. Implementation
We implemented a system as a multi-threaded system forusing multiple CPU cores effectively. In the experiments, weuse a common 4 cores CPU and 4 threads smartphone. We
parallelized extraction for HOG Patch and Color Patch feature.
Extract descriptor, reduce to dimension by PCA, encoding
into Fisher Vector, power normalization, L2 normalization andclassify with SVMs are carried out over 2 cores in parallel foreach feature, totally over 4 cores in parallel.
In advance, we computed gradient magnitude and orientation for an image. Then we extract HOG Patch descriptors
very fast.
The gradient of Fisher Vector with respect to the meanEq.(4) is deformation as follows to decrease the number ofoperation. The number of local descriptors T is much bigger
than the number of GMM component K and the dimension of
local descriptor D, and calculate Eq.(4) for each component,so effectively.
X
Gµ,i
=√

T
1 1X
γt (i)(xt − µi )πi σi T t=1(6)

Moreover in advance, we computed the term of calculate
posterior probability by GMM and the gradient with respect
to the mean and sigma in off-line, and we create the lookup
table for acceleration (using√ for calculate posterior probability√
log πi −0.5×log |Σi |, 1/ 2πi and 1/σ 2 of Eq.(5) and 1/ πi σiof Eq.(6)).

1

2

3

4

5

6

7

8

9

10

top n candidates
Fig. 2. Classification rate by SURF based BoF, HOG Patch based BoF and
FV with 6 or 8 grid step

We trained SVMs in off-line. And all the parameter values
using recognition steps are loaded on main memory (eigenvalue and eigenvector for PCA, created lookup table, mean of
GMM, weight vectors of SVMs). Although all the values canbe stored on main memory in advance, Fisher Vector is able
to bring better recognition result with even smaller dictionary
than that of BoF. We also set the dimension of feature vectors
smaller reduced by PCA. As a result, memory space required
for Fisher Vector is smaller than the space for codebook forconventional BoF, and in respect of memory Fisher Vector isalso superior to BoF.
Finally, we implemented an Android application and integrate our recognition method into this application. It ischaracterized as follows. When a user point a smartphone
camera toward food items, the system real-time recognizes
100 kinds of food items. Then a fixed time later, update listof recognition result. Finally, the user selects food items fromthe food candidate list and record daily foods.
IV.

E XPERIMENTS

In this section, we describe experimental results regarding
recognition accuracy and processing time. In the experiments,
we prepared one hundred categories food image dataset which
has more than 100 images per categories and all the food itemin which are marked with bounding boxes. The total numberof food images in the dataset is 12,905. Figure 1 shows all thecategory names and their sample photos.
We set the validation data and the test data for eachcategory as 20 images. The rest is the train data, and evaluatedclassification rate 5 trials, randomly changing the images in thefive-fold cross validation manner. Next we measure processing
time of image recognition. We use Samsung Galaxy NoteII(1.6GHz 4 cores, 4 tureads, Android4.1) which is the same as[3].
In this experiments, we compare with 2 types of the existing food recognition system. One is a client-side recognition
system by Kawano et al. [3], and the other is a server-side
recognition system by Matsuda et al [14].
At this point, we refer [3] which carries out real-time recognition on smartphone, and encoded into BoF representation.

rice

tempura
udon

sushi

chicken rice

fried rice

tempurabowl

bibimbap

toastcroissantroll breadraisin breadchip butty

hamburger

pizza

sandwiches

udon noodle

soba noodle ramen noodle beef noodle tensin noodle fried noodle

spaghetti

Japanesestylepancaketakoyakigratinsauteedvegetables

croquette

grilledeggplant

sauteedspinachvegetable
tempuramiso soup

potagesausageodenomeletteriyakigrilled fish

fried fish

grilledsalmonsalmonmeunieresashimigrilled pacificsaurysukiyakisweet andsour pork

lightly
roasted fishsteamed egg
hotchpotch

tempurafried chicken sirloin cutlet nanbanzuke

spicy
ginger pork
chili-flavored
saute
tofu

yakitoricabbage rollomeletegg
sunny-side
up

natto

cold tofu

egg roll

chillednoodlestir-fried beef
and peppers

simmered
pork

boiledchicken and sashimi bowl
vegetables

green saladmacaronisaladchinese soup

beef bowl

rice ball

pizza toast

ganmodoki

jiaozisteak

dried fishstew

steamed meat omelet with
dumpling
fried riceFig. 1.

chicken-’n’-egg pork cutlet
on riceon ricepilaf

eels on rice

cutlet curry

spaghetti
meat sauce

beef curry

fried shrimp potato salad

Japanese tofu
miso
and vegetable pork
soup
chowderdipping
noodlesbeefboiled fish seasoned
with potatoes

hambarg
steak

fish-shapedwithsushi bowl pancake with shrimp
chill source roast chicken
bean jamfrench fries

goyachanpuru

kinpira-stylesauteedburdock

hot dog

mixed rice

100 kinds of food images which are recognition targets in the paper

TABLE I.

C LASSIFICATION R ATE THE TOP 1 AND TOP 5 CANDIDATESBY PROPOSED METHOD , CLIENT SIDE [3] AND SERVER SIDE [14]0.9Classification Rate

0.8

methodSURF-BoF+ColorHistogram [3]
HOG Patch-FV+Color Patch-FV
HOG Patch-FV+Color Patch-FV(flip)
MKL [14]

0.7
0.6
0.5
0.4

Color Histogram
Color Patch-BoF
Color Patch-FV
Color Patch-FV (flip)

0.3
0.2
0.1

TABLE II.

1

2

3

4

5

6

7

8

9

10

top5
68.2
77.6
79.2
76.8

AVERAGE P ROCESS T IME

SURF-BoF+ColorHistogram [3]

0

top1
42.0
49.7
51.9
51.6

HOG Patch-FV+Color Patch-FV

time[sec]
0.26
0.065

ncategory
50
100

top n candidates
Fig. 3. Classification rate by Color Histogram, Color Patch based BoF andColor Patch based FV
1Classification Rate

0.9
0.8
0.7
0.6
0.5
0.4

SURF-BoF+ColorHistogram
HOG Patch-FV+Color Patch-FV
HOG Patch-FV+Color Patch-FV (flip)
MKL

0.3
0.2
0.1
0
1

2

3

4

5

6

7

8

9

10

top n candidates
Fig. 4. Classification rate by proposed method, client side [3] and serverside [14]We build a 500 dimension codebook by k-means clustering,
and we apply soft assignment [15], then we apply χ2 kernelfeature map [16]. Finally it is a 1500 dimension vector. And we
don’t apply PCA for local descriptors of BoF representation.
Figure 2 shows the comparison with SURF-BoF [9], HOG
Patch-BoF and HOG Patch-FV. First, we set that a step ofdense grid sampling is every 8 pixels. The difference top1
and top5 classification rate from SURF-BoF and HOG PatchBoF is only 0.62% and 2.1%, respectively, which means thath

SURF is slight better. However, HOG Patch-FV achieved
higher performance than SURF-BoF, differ is 2.7% and 3.22%.
And then HOG Patch extraction is very fast than SURF
extraction. In this paper, we set that a step of dense grid
sampling is every 6 pixels. Then improved classification rateand in case of adding horizontally flipped images for training
data, we achieved 36.3% and 63.2% classification rate with
top1 and top5 candidates. The classification rate is 7.52% and6.9% higher than SURF-BOF.
Next, we evaluated Color Patch feature. Figure 3 shows
the color histogram, Color Patch-BoF and Color Patch-FV.
Color histogram divides a given image into 3 × 3 blocks, andextract a 64-bin RGB color histogram from each block. Totally,
we extract a 576-dim color histogram. Then apply χ2 kernelfeature map. Finally we build a 1728 dimension. The difference
top1 and top5 classification rate from Color Patch-BoF andcolor histogram is only 0.76% and 2.28%, Color Patch-BoF
is slight better. But in case of Color Patch-FV, classification
rate is much improved, and top1 and top5 classification rate
is 13.0% and 18.4% higher than color histogram. In caseof adding horizontally flipped images for training data, we
achieved 43.0% and 70.6% classification rate.
Then we compare recognition accuracy with exist image recognition system. Our proposed method (HOG PatchFV+Color Patch-FV) and (HOG Patch-FV+Color PatchFV(flip)), client side recognition method [3](SURF-BoF+color
histogram) and server side recognition method [14]. [14] adopt
total 5 features including hard assignment BoF and globalfeature based feature and classify with nonlinear χ2 RBF
kernel MKL-SVM, which is very high computational cost.

Figure 4 shows classification rate of each recognition method
and Table I shows classification rate with top1 and top5. Our
HOG Patch-FV+Color Patch-FV achieved 49.7% and 77.6%
classification rate with top1 and top5 and in case of adding
flipped images, it achieved 51.9% and 79.2% classification
rate with top1 and top5. In case of using only Color Patchfeature, we achieved better classification rate than [3], whichmeans that Fisher Vector representation for Color Patch feature
is very effective for food recognition. And our approach is
better than [14] which server side very high cost recognition
system. Therefore we show efficacy our recognition method
and it suggests that it is able to carry out high precision image
recognition on smartphone.
Next, we measured recognition time by repeating 20 times
and averaging them. Table II shows results of average recognition time. We also show experiment value of [3] for 50 kindsof image recognition. Our approach takes 0.065 seconds for
100 kinds. On the other hand, [3] takes 0.26 seconds for 50
kinds. Thus proposed method is very fast than [3], and showed
proposed method suitable for rapid image recognition.
Regarding memory space, [3] adopt 1500-dim SURF-BoF
and 1728-dim color histogram. On the other hand, our adopt
1536-dim HOG Patch-FV and 1536-dim Color Patch-FV. The
feature vector is more compact but more dense. And then many
values are loaded on memory to encode Fisher Vector faster,sum of these require only less one fifth in case of HOG Patchand less one fourth in case of Color Patch than memory spaceof codebook for BoF, respectively. Java heap (mainly except
image processing) and native heap (mainly image processing)the implemented application required were about 16MB and
3MB. We realize the mobile system with low memory space.
Therefore we are able to increase the number of recognition
target and feature dimension.
According to the experiments of recognition accuracy andprocessing time, we showed efficacy our proposed method.
And we achieved better result than server side very high cost
recognition method [14]. Therefore we showed rapid image
recognition and high precision on smartphone is possible.
V.

C ONCLUSION

We proposed image recognition method for run on a common smartphone that HOG Patch and Color Patch descriptor
as local feature, then they are Fisher Vector represented, finallyclassify with linear SVM.
In the experiments, we have achieved 79.2% classification
rate with the top five candidates when ground-truth boundingboxes are given. It is higher 11.0% than recognition method
carries out on a smartphone that adopt color histogram and
SURF-BoF with soft assignment and apply χ2 kernel feature
map. And it is also higher than very high cost server siderecognition method. In addition, processing time is 0.065
second for 100 kinds of target. It is faster 75.0% than 0.26
of [3]. We showed efficacy of proposed method and rapid andhigh precision image recognition on a smartphone.
As a feature works, we plan to extend the method andsystem regarding the following issues. Increase recognition
targets by using crowd-sourcing and run large scale image

recognition on a smartphone. Then efficient classify by hierarchical classify. Not only run rapid image recognition, butalso run rapid interest object detection.
Note that Android application of the implemented mobile food recognition system can be downloaded from http://foodcam.mobi/.
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