Automatic Forest Species Recognition based on

Multiple Feature Sets

Marcelo N. Kapp, Rodrigo Bloot

Universidade Federal da Integracao

Latino-Americana - UNILA

Foz do Iguacu, PR, Brazil

e-mail: {marcelo.kapp,rodrigo.bloot}@unila.edu.br

Paulo R. Cavalin

IBM Research Brazil

Rio de Janeiro, RJ, Brazil

e-mail: pcavalin@br.ibm.com

Luiz E. S. Oliveira

Universidade Federal do

Parana - UFPR

Curitiba, PR, Brazil

email: lesoliveira@inf.ufpr.br

Abstract—In this paper we investigate the use of multiplefeature sets for automatic forest species recognition. In order toaccomplish this, different feature sets are extracted, evaluated,and combined into a framework based on two approaches: imagesegmentation and multiple feature sets. The experimental resultson microscopic and macroscopic images of wood indicate that therecognition rates can be improved from 74.58% to about 95.68%and from 68.69% to 88.90%, respectively. In addition, theyreveal us the importance of exploring different window sizes andappropriate local estimation functions for the LPQ descriptor,further than the classical uniform and gaussian functions.

I. INTRODUCTION

The development of automatic solutions for the recognition

of forest species is a challenge and interesting problem, due

to its both commercial and environment-preserving value.

This kind of systems can help to better monitor wood tim-

ber trading, may reduce commercialization of samples from

species that are forbidden to be traded, or still species near

extinction. In addition, an automatic system represents a low-

cost alternative, since it could reduce the costs for hiring and

training human experts.

Recently various systems have been proposed to deal with

this problem [1], [2], [3], [4], [5], [6]. In fact, we have

seen that the use of multiple feature vectors is promising to

reduce recognition error [5], [6]. In multiple feature vectors

approaches, an input image is firstly divided into N sub-

images and N distinct feature vectors are extracted, then each

vector is individually classified and has their classification

outputs combined in order to compute the final recognition

decision. Such approach usually outperforms baseline systems

considering only one feature vector extracted from the original

image.

In this work, we investigate the use of multiple feature

vectors based on a quadtree-based framework[6]. The frame-

work considers that multiple feature vectors can be extracted

by both segmenting the input image and by considering

different feature sets. In resume, the original image is divided

according to a quadtree-based approach, then different feature

sets (Linear Binary Patterns and Local Phase Quantization),

and four common combination rules (Sum, Product, Max,

and Majority Voting rules) are applied. Motivated by the

performance reached by the LPQ (Local Phase Quantization)

feature extractor method in our previous work [6], in here we

also propose a local estimation strategy based on Blackman

function (i.e. LPQ-Blackman) and explore different window

sizes.

The experimental evaluation demonstrates that the proposed

approach is not database-dependent and can work well on

databases with different properties. On a database with mi-

croscopic images from 112 different species of wood, these

experiments show that the recognition rates can increase from

74.58%, extracting a single LPQ feature vector, to 95.68%,

extracting both LBP, LPQ, and LPQ-Blackman features and

dividing the image into 16 segments. Similar evaluations

conducted on macroscopic images demonstrate that the recog-

nition rates can be raised from 68.69% to 88.90% using LPQ-

Blackman features and dividing the image into 16 segments.

The remainder of this paper is organized as follows. We de-

scribe the framework and features sets employed in Section II.

The experimental protocol and obtained results are reported in

Section III. Then, conclusions and future work are discussed

in Section IV.

II. THE MULTIPLE FEATURE SETS FRAMEWORK

The framework uses information from multiple feature

vectors to improve recognition performance, owing to the

variability introduced by these multiple vectors. The idea is

to extract varied feature vectors by both dividing the input

image into segments, and by combining different feature sets.

In this section, we firstly provide details on the framework,

then we summarize a collection of feature sets and the fusion

functions to be employed.

The general idea of the framework is listed in Algorithm 1.

The inputs for this algorithm are: the original image, denoted

im; the parameter L, to define the number of segments

in which im will be divided; the set of feature sets Γ ={γ1, . . . , γM

}, where γj represents a distinct feature set with

mj features, and M = |Γ| is the total number of feature sets;

the ensemble of classifiers CL = {c1L, . . . , cML } correspond

to the set of classifiers generated for a given value of L

for each γj ; the total number of classes of the problem are

denoted as K; and a combination function λ. From these

inputs, the main steps are: (1) To divide the input image into

N segments; (2) To compute the number of segments N as

2014 International Joint Conference on Neural Networks (IJCNN) July 6-11, 2014, Beijing, China

978-1-4799-1484-5/14/$31.00 ©2014 IEEE 1296

a function of L, e.g. N = f(L); (3) To divide im into N

non-overlapping segments with identical sizes, and to generate

the set I = {im′

1, . . . , im′

N}. Then, the feature extraction

procedure is conducted. In (4) to (8), for each image im′

i in I

and each feature set γj in Γ, the feature vector vji is extracted

and saved in V . Thereafter, each feature vector vji in V is

classified by the corresponding classifier cjL, resulting in the

scores pji (k) for every class k, where 1 ≤ k ≤ K. Finally, all

the scores pji (k) are combined using the combination function

λ and the final recognition decision φ is made, i.e. the forest

species (a class k) from which im has been extracted is

outputted. The image segmentation approach, as well as the

features sets and combination rules employed are described

below.

Algorithm 1 The main steps of the multiple feature vector

framework[6].

1: Input: im, the input image; L, the parameter to compute

the number of segments to divide im; Γ = {γ1, . . . , γM},

the collection of feature sets, where ξj corresponds to

a distinct feature set; CL = {c1L, . . . , cML }, the set of

classifiers trained for the given L, for each ξj ; K, the

number of classes of the problem; and λ, a fusion function

to combine multiple classification results.

2: N = f(L)3: Divide im into N non-overlapping sub-images with equal

size, generating the set I = {im′

1, . . . , im′

N}

4: for each image im′

i in I do

5: for each feature set γj in Γ do

6: Extract feature vector vji from imi by considering ξj

as feature set, and save vji in V

7: end for

8: end for

9: for each feature vector vji in V do

10: Recognize vji using classifier c

jL, and save the scores

pji (k) for each class k, where 1 ≤ k ≤ K.

11: end for

12: Combine all scores pji (k) using λ, and compute the final

recognition decision φ.

A. Quadtrees for Image Segmentation

As described in [7], [8], a quadtree recursively divides an

input image into four segments according to the number of

levels of the three. It means that for each level L, where L ≥ 1,

the input image, denoted im, is divided into (2L−1)2

non-

overlapping sub-images of equal sizes. This way, the original

image remains the same at level 1 im, at level 2 it is divided

in 4 images, at level 3 in 16 images, and so on.

As a result, a quadtree allows for evaluating a given image at

different granularity levels, resulting in a hierarchical approach

for image segmentation. In this work, nonetheless, such rela-

tionships are not important since we are just interested in the

exponential function used by quadtrees to divide images. As a

result, the parameter L of Algorithm 1 is employed to compute

the number of segments (step 2) according to the correspond-

ing level L of the quadtree, e.g. N = f(L) = (2L−1)2.

B. Feature Sets

Two distinct feature sets are evaluated: Linear Binary

Patterns and Local Phase Quantization. LBP and LPQ are

currently among the best feature sets for such a task. In

addition, we present a local estimation strategy for the LPQ

method.

1) Local Binary Patterns - LBP: This technique was intro-

duced by Ojala et al. [9]. In the LBP method, a given pixel

in the image is labelled by thresholding a 3 neighborhood of

each pixel with the center value. More specifically, the LBP

method can be denoted as:

LBP =P−1∑

j=0

m(gp − gc)2P , (1)

where

m(x) =

{

1, if x ≥ 00, otherwise.

(2)

gc represents the central pixel value and the value in

its neighborhood is represented by gp. P is the number of

neighbors used. For example, if we take P = 8 the LBP

method generates 28 = 256 different binary patterns that can

be formed by the pixels in the neighborhood set. By contrast,

uniform binary patterns can represent a suitable amount of

information by a reduced set of binary patterns, since that

they can be denoted by 58 binary patterns. Indeed, they might

account for nearly 90% of all patterns in the neighborhood

[9].

By definition, a binary pattern is called uniform if it contains

at most two 0/1 transitions when the binary string is considered

circular. In here, we consider a LBP feature extractor that

produces a 59-position normalized histogram. The first 58

positions represent the frequencies of an uniform pattern and

the 59-th position defines the occurrence of all non-uniform

ones.

2) Local Phase Quantization - LPQ: Local Phase quanti-

zation (LPQ) was proposed by Ojansivu et Heikkila [10] and

is related to Discret Fourier Transform (DFT). This technique

calculates the convolution of the image f(x) from the function

e−2πjξTx for each pixel position x. For sake of simplicity, we

denotes x = (x1, x2) for vector position and ξ = (ξ1, ξ2) for

the vector in Fourier domain. Therefore, the convolution takes

the form for all ξ:

h(x) = f(x) ∗ e−2πjξTx (3)

Concerning the LPQ method, this convolution is not cal-

culated over all two-dimensional Euclidean plane, but from a

window M ×M denoting the neighborhood Nx of each pixel

x. Therefore, the Equation (3) can be rewritten as:

h(ξ,x) =

∫ ∫

y∈Nx

f(x− y)e−2πjξTydy (4)

1297

In this technique, four complex coefficients are used. They

correspond to the 2-D frequencies ξ1 = (α, 0)T ,ξ2 = (0, α)T ,

ξ3 = (α, α)T and ξ4 = (α,−α)T , where α is a scalar

frequency set in terms of window size. Once the convolutions

are computed over ξ1, ξ2, ξ3 and ξ4, a 8-position vector Gx is

generated and quantizated according to the following process:

qj =

{

1, if gj ≥ 00, otherwise.

(5)

Where gj is the the j-th component of Gx and

b =8

∑

j=1

qj2j−1. (6)

From Equation 6, the quantized coefficients are converted to

integer values between 0-255 and placed ino a 256 histogram,

similar to the LBP method (see Equations 1 and 2). The

accumulated values in the histogram will be used as the LPQ

256-dimensional feature vector. A complete description of this

method can be found in [10].3) On LPQ Spectral Leakage and Weight Function:

Equation (4) represents the Short Term of Discrete Fourier

Transform (SDFT) of the image f(x) in a neighborhood of a

pixel x. However, if one regards a SDFT over the entire image,

there are some concerns about spectral leakage that must be

taken into account.

The spectral leakage occurs because there are discontinuities

in the signal periodic extension, since that it is related to

the finite sampling duration. These facts implies spectral

contributions in the Fourier domain. In LPQ for each pixel

position x, a SDFT is calculated over a finite neighborhood

Nx. Therefore, the spectrum can contain values that are not

essentially related to the pixels of the image. It means that

for each Nx, such values will affect the calculation of the

final feature vectors and hence contribute negatively to the

recognition process.

Concerning that the effects of spectral leakage can be reduce

by using a convenient weight function Wr(x) for DFTs [11],

in this paper we suggest the application of a weight function

defined by:

Wr(x) = W1(x1)W2(x2) (7)

with

Wi(xi) = 0.42 + 0.5cos(πxi

σ) + 0.08cos(

2πxi

σ), (8)

for i = 1, 2 and σ given in terms of Nx. More precisely, a

σ = (M −1)/4 is set for a window size M ×M . This weight

function (8) has the same algebraic format of Blackman weight

function ( see [11] ). The difference is that it is not null for

|xi| > σ, as illustraded in Figure 1.

From here, we will call this local estimation strategy as

LPQ-Blackman. In contrast, while Wi(xi) = 1 represents

the classical uniform local estimation function (i.e. standard

LPQ), the gaussian local estimation function (i.e. LPQ-Gauss)

is depicted by:

Wi(xi) =e−0.5(

xi

σ)2

√

2πσ(9)

Ending, the Equation 4 with the window Wr takes the form:

h(ξ,x) =

∫ ∫

y∈Nx

Wr(y)f(x− y)e−2πjξTydy (10)

−4 −3 −2 −1 0 1 2 3 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Window

Fu

nctio

n

Fig. 1. illustration of extension for the Blackman weight function withσ = 2.

C. Combination Functions

Let pji (k) be the score of class k outputted by classifier

cjL given the feature vector v

ji , we consider the following

combination functions to compute the final decision φ.

1) Sum rule:

φ =K

argmaxk=1

N∑

i=1

M∑

j=1

pji (k) (11)

2) Product rule

φ =K

argmaxk=1

N∏

i=1

M∏

j=1

pji (k) (12)

3) Max rule

φ =K

argmaxk=1

Nmaxi=1

Mmaxj=1

pji (k) (13)

4) Majority Voting

∆ji (k) =

{

1, if pji (k) = maxKk=1

pji (k)

0, otherwise.(14)

φ =K

argmaxk=1

N∑

i=1

M∑

j=1

∆ji (k) (15)

See Kittler et al. [12] or Tax et al. [13] for more details.

1298

III. EXPERIMENTAL EVALUATION

A. Databases and Setup

For the experimental evaluation of the methods described

in this paper, we make use of two Forest Species databases

dubbed microscopic database and macroscopic database, re-

spectively1.

The microscopic database, presented in [3], contains 2,240

microscopic images equally distributed in 112 distinct classes

of forest species, where 37 species consist of Softwood and

75 consist of Hardwood species. Generally softwood and

hardwood present different texture structures. These images

have a resolution of See in Figure 2 some samples of the

images found in this database.

(a) Anadenanthera colubrina (b) Cedrus sp

(c) Ginkgo biloba (d) Cariniana estrellensis

Fig. 2. Samples from the microscopic database.

The macroscopic database is composed of 2,942 samples of

41 distinct species. These images were captured with a Sony

DSC T20 digital camera, with a 3,264 by 2,448 resolution.

The number of samples per class ranges from 37 to 99, with

an average of 71.75. Some samples of this database can be

seen in Figure 3.

For these experiments, the samples of each class have been

partitioned in: 50% for training; 20% for validation; and 30%

for test. Each subset has been randomly sampled, with no

overlapping between the sets.

For avoiding the results to be biased to a given partitioning,

this scheme is repeated 10 times. As a consequence, the

results presented further represent the average recognition rate

over 10 replications (each replication is related to different a

partitioning).

As the base classifier, we use Support Vector Machines

1The databases are freely available in http://web.inf.ufpr.br/vri/image-and-videos-databases/forest-species-database and http://web.inf.ufpr.br/vri/image-and-videos-databases/forest-species-database-macroscopic

(a) Aspidosperma polyneuro (b) Hura crepitans

(c) Podocarpus lambertii (d) Vochysia sp

Fig. 3. Samples from the macroscopic database.

(SVMs) with Gaussian kernel2. Parameters C and γ were

optimized by means of a grid search with hold-out validation,

using the training set to train SVM parameters and the

validation set to evaluate the performance. After finding the

best values for C and γ, an SVM was trained with both the

training and validation sets together. Normalization was used

by means of linearly scaling each attribute to the range [-1,+1].

Due to limitation of space, in the remainder of this section we

present only the results of the system that uses the Product

rule as combination function. This function achieved the best

results.

B. Results on the microscopic database

The first set of experiments consisted of investigating the

impact, in terms of recognition accuracy, of the window size

to extract LPQ features (i.e. with uniform local estimation

function). Also, we would to compare the performance of these

feature sets. For doing so, the system was evaluated with a

single feature at once and with L set to 1, a single feature

set is extracted from the image. Window with sizes of 3, 5, 7

and 9 pixels, were considered. The results are summarized in

Figure 4. It is remarkable that, for all feature sets, there is a

significant increase of performance as the size of the window

increase, specially by comparing the sizes 3 and 5. From 5 to

7 and 9 there is also gain in accuracy, but such gain is less

prominent. In comparing the feature sets, we observe that LPQ

results in the highest recognition rates with windows of size

3. LPQ-Gauss (i.e with gaussian local estimation function), on

the other hand, present the worst performance with the smallest

window. As the window is enlarged, however, LPQ and LPQ-

Gauss tend to present more similar performances. It is worth

mentioning that LPQ-Blackman outperforms both with larger

2in this work we used the LibSVM tool available athttp://www.csie.ntu.edu.tw/˜cjlin/libsvm/.

1299

windows. The best recognition rates in these experiments were

achieved by the LPQ-Blackman feature set with window size

9: 88.58%. With the same setup, LPQ reached the second best

result with 86.35%. This indicates that LPQ-Blackman might

be a better feature set for this problem.

L1-3

L1-5

L1-7

L1-9

B1-3

B1-5

B1-7

B1-9

G1-3

G1-5

G1-7

G1-9

74

76

78

80

82

84

86

88

90

Reco

gnit

ion r

ate

(%

)

Fig. 4. Evaluation of different window sizes for LPQ (L), LPQ-Blackman(B) and LPQ-Gauss (G), considering the entire image (L = 1)

The superior performance of LPQ-Blackman was confirmed

in other experiments. In Figure 5 we present the results of

the evaluation of two different window sizes (3 and 9) by

taking advantage of the multiple feature vector framework.

In this case we present the evaluation of a few combinations

(individually, pairwise and all at once) of LPQ, LPQ-Blackman

and LPQ-Gauss with L = 2. With window of size 3, LPQ

achieved the best results with about 91.51%, likewise to what

we observed with L = 1. Nevertheless, with window size set to

9, LPQ-Blackman is the best feature set with recognition rates

of about 95.10%. In addition, by combining more that at least

two feature sets one can observe a slight improvement in the

accuracy. The best results were achieved with the combination

of the three feature sets, with recognition rates of about

95.28%. With L = 1, as shown in Figure 6, we observed that

the different approaches behave similar to what we observed

with L set to 2. With L = 1, the best recognition rates are

observed with the combination of LPQ and LPQ-Blackman:

93.83%. Notice that generally the highest recognition rates are

achieved by the systems that include LPQ-Blackman.

The impact of the combination of LPQ feature sets with

LBP is presented in Figure 7. We observe that, individually,

LBP generally achieves the lowest recognition rates, which

may makes us believe that using only LPQ features might

be a better alternative. However, when combined with one of

the LPQ features some boost can be observed. For instance,

with L = 1 LBP reaches 74.58% and LPQ (window size set

to 9) 86.35%. When both feature sets are combined, then the

recognition rates rise to 88.11%. This highlights the fact that

L-3

L-9

B-3

B-9

G-3

G-9

LB-3

LB-9

LG-3

LG-9

BG

-3

BG

-9

LBG

-3

LBG

-9

86

88

90

92

94

96

98

Reco

gnit

ion r

ate

(%

)

Fig. 5. Results considering different combinations of the feature sets LPQ(L), LPQ-Blackman (B) and LPQ-Gauss (G), with window sizes of 3 or 9and L set to 3.

L-3

L-9

B-3

B-9

G-3

G-9

LB-3

LB-9

LG-3

LG-9

BG

-3

BG

-9

LBG

-3

LBG

-9

82

84

86

88

90

92

94

96R

eco

gnit

ion r

ate

(%

)

Fig. 6. Results considering different combinations of the feature sets LPQ(L), LPQ-Blackman (B) and LPQ-Gauss (G), with window sizes of 3 or 9and L set to 2.

the combination of complementary feature sets might improve

the overall performance of the system. In these experiments,

the highest recognition rates are achieved with the combination

of the four feature sets: 95.68%. In order to further evaluate

the benefits of combining complementary feature sets, in

Figure 8 we present the combination of LPQ feature sets with

different window sizes. That is, besides combining different

types of LPQ feature extractors, we also combine features

extracted with windows of size 3 and 93. These combination

3Preliminary experiments were carried out to combine windows of size 5and 7 also, but no gains were observed.

1300

P1

P2

P3

PL1

PL2

PL3

PB

1

PB

2

PB

3

PG

1

PG

2

PG

3

PLB

G1

PLB

G2

PLB

G3

70

75

80

85

90

95

100

Reco

gnit

ion r

ate

(%

)

Fig. 7. Results of LBP (P) individually and combined with LPQ (L), LPQ-Blackman (B) and LPQ-Gauss (G) with window size 9 and L set to 3.

resulted in the most significant boost in performance from

the combination of different feature sets. The best recognition

rates were achieved with the combination of LPQ and LPQ-

Blackman: 95.68%.

L1 L2 L3 B1

B2

B3

G1

G2

G3

LB1

LB2

LB3

LG1

LG2

LG3

BG

1

BG

2

BG

3

LBG

1

LBG

2

LBG

3

84

86

88

90

92

94

96

98

Reco

gnit

ion r

ate

(%

)

Fig. 8. Evaluation of the combination of the LPQ feature sets with differentwindow sizes, in this case the sizes 3 and 9. The parameters L lies in therange 1-3.

A summary of these results are list in Table I. Compared

with the literature, the results obtained in this work are the

best so far on this database. The best previously reported

recognition rates where of 93.2% [6] and 86.47% [4].

TABLE ISUMMARY OF RESULTS - MICROSCOPIC.

Method Recognition rates (%)

Single Feature Set/Entire Image

LBP 74.58LPQ 86.35LPQ-Blackman 88.58LPQ-Gauss 85.44

Multiple Feature Sets/Entire Image

LBP + LPQ 88.11LBP + LPQ-Blackman 89.13LBP + LPQ-Gauss 86.92LPQ + LPQ-Blackman 89.37LPQ + LPQ-Blackman + LPQ-Gauss 89.92

Multiple Window Sizes/Entire Image

LPQ 88.92LPQ-Blackman 89.79LPQ-Gauss 86.94LPQ + LPQ-Blackman 90.31LPQ + LPQ-Gauss 88.67LPQ + LPQ-Blackman + LPQ-Gauss 89.71

Multiple Window Sizes/Multiple Segments (L = 3)

LBP 88.67LPQ 95.28LPQ-Blackman 95.58LPQ-Gauss 94.58LBP + LPQ 94.56LBP + LPQ-Blackman 95.23LBP + LPQ-Gauss 94.25LBP + LPQ + LPQ-Blackman + LPQ-Gauss 95.68LPQ + LPQ-Blackman 95.74LPQ + LPQ-Gauss 95.20LPQ + LPQ-Blackman + LPQ-Gauss 95.68

C. Results on the macroscopic database

In Figure 9 we present the results of the evaluation of

each feature set individually on the entire image. Given the

results on the macroscopic database, we present the evaluation

windows with size 3 and 9 only. In this case we do not

observe any gain with LPQ-Blackman. LPQ with window size

9 achieved the best results with 81.66%, which is slightly

better than LPQ-Blackman with 81.42%. On the other hand,

we observe a significant increase of performance with the

increase of the size of the window, as observed with the other

database.

The performance of LPQ-Blackman stands out when the

combination of segments is considered. The use of this feature

set individual achieved recognition rates of about 88.90%,

while LPQ was the second best feature set with 81.84%, with

L = 3 in both cases. The combination of different feature sets

did not result in any improved, but when LPQ-Blackman is

used they stay close to the best results. The results for the

other feature sets and combinations, with L = 3, are shown in

Figure 10. For L = 2 the results are presented in Figure 11.

In Figure 12 and Figure 13 we present the combination

of the LPB feature sets with LBP and the combination of

windows with size 3 and 9, respectively. Nevertheless, none

of these system outperformed the use of LBP-Blackman indi-

vidually with L = 3 (88.90%). The best results with LBP were

of about 82.96%, combined with LBP. With the combination

of different window sizes, the best results were achieved by

1301

L1-3

L1-9

B1-3

B1-9

G1-3

G1-9

68

70

72

74

76

78

80

82

84R

eco

gnit

ion r

ate

(%

)

Fig. 9. Evaluation of different window sizes for LPQ (L), LPQ-Blackman(B) and LPQ-Gauss (G), considering the entire image (L = 1)

L-3

L-9

B-3

B-9

G-3

G-9

LB-3

LB-9

LG-3

LG-9

BG

-3

BG

-9

LBG

-3

LBG

-9

78

80

82

84

86

88

90

92

Reco

gnit

ion r

ate

(%

)

Fig. 10. Results considering different combinations of the feature sets LPQ(L), LPQ-Blackman (B) and LPQ-Gauss (G), with window sizes of 3 or 9and L set to 3.

LPQ-Blackman with 88.08%.

In Table II the summary of the results on this database are

presented. The best results achieved in this work (88.90%) are

slightly above the best ones published in the literature con-

sidering gray-scale textural features, i.e. 88.60% with CLBP

features and images divided into 25 segments [14]. Never-

theless, by comparing the results of systems with the same

number of segments, the superior performance of the proposed

LPQ-Blackman feature set becomes clearer. By dividing the

image into 16 segments, we achieved 88.90% against 86.24%

reported in [14]. This indicates that by increasing the number

of segments even better recognition rates might be achieved.

L-3

L-9

B-3

B-9

G-3

G-9

LB-3

LB-9

LG-3

LG-9

BG

-3

BG

-9

LBG

-3

LBG

-9

74

76

78

80

82

84

86

88

90

Reco

gnit

ion r

ate

(%

)

Fig. 11. Results considering different combinations of the feature sets LPQ(L), LPQ-Blackman (B) and LPQ-Gauss (G), with window sizes of 3 or 9and L set to 2.

P1

P2

P3

PL1

PL2

PL3

PB

1

PB

2

PB

3

PG

1

PG

2

PG

3

PLB

G1

PLB

G2

PLB

G3

60

65

70

75

80

85R

eco

gnit

ion r

ate

(%

)

Fig. 12. Results of LBP (P) individually and combined with LPQ (L), LPQ-Blackman (B) and LPQ-Gauss (G) with window size 9 and L set to 3.

IV. CONCLUSION AND FUTURE WORK

In this work we investigated the use of multiple feature sets

for forest species recognition. Two main different features sets

(LBP and LPQ) were extracted, evaluated, and combined into

a framework based on two approaches: image segmentation

and multiple feature sets. The obtained results demonstrated

that, on microscopic images, recognition rates can be improved

from 74.58% (with a single feature vector) to about 95.68%

using such multiple feature set approach. Compared with the

literature, these results were the best found so far for the

database tested.

1302

L1 L2 L3 B1

B2

B3

G1

G2

G3

LB1

LB2

LB3

LG1

LG2

LG3

BG

1

BG

2

BG

3

LBG

1

LBG

2

LBG

3

78

80

82

84

86

88

90R

eco

gnit

ion r

ate

(%

)

Fig. 13. Evaluation of the combination of the LPQ feature sets with differentwindow sizes, in this case the sizes 3 and 9. The parameters L lies in therange 1-3.

TABLE IISUMMARY OF RESULTS - MACROSCOPIC.

Method Recognition rates (%)

Single Feature Set/Entire Image

LBP 68.69LPQ 81.66LPQ-Blackman 81.41LPQ-Gauss 79.51

Multiple Feature Sets/Entire Image

LBP + LPQ 75.44LBP + LPQ-Blackman 76.18LBP + LPQ-Gauss 72.79LPQ + LPQ-Blackman 73.78LPQ + LPQ-Blackman + LPQ-Gauss 83.69

Multiple Window Sizes/Entire Image

LPQ 81.84LPQ-Blackman 88.07LPQ-Gauss 81.84LPQ + LPQ-Blackman 86.48LPQ + LPQ-Gauss 81.84LPQ + LPQ-Blackman + LPQ-Gauss 85.36

Multiple Window Sizes/Multiple Segments (L = 3)

LBP 79.75LPQ 81.84LPQ-Blackman 88.90

LPQ-Gauss 81.84LBP + LPQ 82.96LBP + LPQ-Blackman 82.54LBP + LPQ-Gauss 82.96LBP + LPQ + LPQ-Blackman + LPQ-Gauss 82.49LPQ + LPQ-Blackman 88.56LPQ + LPQ-Gauss 81.84LPQ + LPQ-Blackman + LPQ-Gauss 87.44

On the macroscopic database we also observed significant

increase in performance with the use of the proposed ap-

proaches.

In order to reduce the possible effect of spectral leakage

regarding the LPQ feature extractor method with the clas-

sical uniform and guassian local estimation functions, we

introduced a strategy based on the Blackman function. The

obtained results indicated that the use of this function was

very advantageous. It guaranteed the highest performances for

this database, mainly when its results were combined into a

multiple feature set framework. In addition, the results also

revealed the importance of exploring and combining different

window sizes. As future work, it might be interesting the

deeper analysis of methods for local estimation. In addition,

the quadtree-based approach can be employed as a hierarchical

approach to combine multiple feature vectors.

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