Visual Objects Classification with Sliding Spatial Pyramid Matching
Authors: Hao Wooi Lim, Yong Haur Tay
ICLR 2013

Abstract: We present a method for visual object classification using only a single feature, transformed color SIFT with a variant of Spatial Pyramid Matching (SPM) that we called Sliding Spatial Pyramid Matching (SSPM), trained with an ensemble of linear regression (provided by LINEAR) to obtained state of the art result on Caltech-101 of 83.46%. SSPM is a special version of SPM where instead of dividing an image into K number of regions, a subwindow of fixed size is slide around the image with a fixed step size. For each subwindow, a histogram of visual words is generated. To obtained the visual vocabulary, instead of performing K-means clustering, we randomly pick N exemplars from the training set and encode them with a soft non-linear mapping method. We then trained 15 models, each with a different visual word size with linear regression. All 15 models are then averaged together to form a single strong model.

arXiv:1212.3767v2  [cs.CV]  18 Dec 2012arXiv: some other text goes here
Visual Objects Classiﬁcation with Sliding Spatial
Pyramid Matching
Hao Wooi Lim 1 and Yong Haur Tay 2
1 haowooilim@1utar.my, 2 tayyh@utar.edu.my
Computer Vision & Intelligent Systems (CVIS) group, Faculty of Engineering and Science,
University of Tunku Abdul Rahman
Abstract
We present a method for visual object classiﬁcation using on ly a single feature,
transformed color SIFT [15] with a variant of Spatial Pyrami d Matching (SPM) that
we called Sliding Spatial Pyramid Matching (SSPM), trained with an ensemble of linear
regression (provided by LINEAR) to obtained state of the art result on Caltech-101 [22]
of 83.46%. SSPM is a special version of SPM where instead of di viding an image into
K number of regions, a subwindow of ﬁxed size is slide around th e image with a ﬁxed
step size. For each subwindow, a histogram of visual words is generated. To obtained
the visual vocabulary, instead of performing K-means clust ering [26], we randomly pick
N exemplars from the training set and encode them with a soft no n-linear mapping
method from [16]. We then trained 15 models, each with a diﬀere nt visual word size
with linear regression. All 15 models are then averaged toge ther to form a single strong
model.
1 Introduction
Recently, there has been a lot of development in the area of Deep Le arning (DL). Not unlike
traditional Multi-layer Neural Networks, DL sought to solve the diﬃ cult problems such as
visual objects classiﬁcation by utilizing many layers of neurons, whic h has been traditionally
too computationally intensive to train, with the help of fast, many-c ores Graphics Processing
Unit (GPU) to help speedup training. One interesting outcome of the research in DL is such
that, DL allows for learning of feature detectors, thus mostly elimin ating the need for hand-
engineered features. And sure enough, recent papers such as [1 8] has shown that DL could
even obtained state of the art results on various visual object da ta set such as MNIST [19],
NIST SD 19 [20], CIFAR-10 [21] and etc. However, noticed that most of the data set tested
are usually consists of very small images. This is due to the fact that data set with huge
dimensionality would pose an even bigger challenge for GPU, sometimes even requiring weeks
of training, even with a cluster of GPUs. Moreover, not everyone h as a huge cluster of GPUs
at their disposal.
Looking at some of the best-performing methods ([1], [4], [8], [9]), sho wn on Fig. 1 on
Caltech-101 [22], one trend seems apparent: many diﬀerent featu res are usually employed
to obtained the published results, most notably Scale-invariant fea ture transform (SIFT)
[23], Pyramid histogram of orientated gradients (PHOG) [9], Self-sim ilarity (SSIM) [25],
Geometric Blur (GB) [24] and etc. While it is easy to obtain better accu racy by employing
many diﬀerent features, it signiﬁcantly increases the computation power required to train
them.
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Figure 1: Figure compares the accuracy on Caltech-101 data set, all trained on 30 samples.
Some algorithm like kernel-based Support Vector Machine (SVM) whic h requires O(nd)
testing time, where n is the number of support vectors and d is the dimensionality of the
feature vector [27], it is just not feasible when the model is complex, resulting in large number
of support vectors or when the feature dimension is large. Unfort unately, it is pretty easy to
feed a lot of features for training, allowing the training process to d iscover salient features
useful for accurate classiﬁcation. Thus, in most cases, it is diﬃcult to obtain good results
with kernel-based SVM without also making training intractable.
In this paper, our goal is to lower the error bound siginiﬁcantly on ch allenging visual
object data set such as Caltech-101 [22] that has high enough num ber of categories and
dimensionality, while also employing methods that are much more eﬃcien t to train than
DL-based methods, relying on hand-engineered feature but refr ained from combining many
diﬀerent kind of features so that training remains quick and eﬃcient .
2 Our method
Here, we describe our method, along with the best parameters tha t we have found.
Our method begins with extraction of features. Following the metho d employed by [15],
we use Harris-Laplace salient point detector to localize all the local k eypoints in each image.
For each keypoint, we use the Transformed Color SIFT as the desc riptor.
To obtained a visual vocabulary, we randomly pick N exemplars from the training set
instead of performing K-means clustering [26], which was the usual m ethod employed by
most paper.
To encode keypoint into feature vector, we encode them with a sof t non-linear mapping
method as described in [16].
Next, we use a special version of Spatial Pyramid Matching (SPM) wh ere instead of
dividing an image into K number of regions whereby for each region, a histogram of visual
words is generated, a subwindow of ﬁxed size is slide around the image with ﬁxed step size.
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Each subwindow then, is process the same as you would with a region in SPM. We call
this Sliding Spatial Pyramid Matching (SSPM). Here, we set the width a nd height of the
subwindow to be one-third of the original image’s width and height res pectively, while step
size x and y to be the one-third of the subwindow’s width and height re spectively.
Since for each region we obtained a feature vector with size equal t o the visual vocabulary
size, we append each feature vector from each region into a single f eature vector to be trained
by linear regression.
To train the system, we used LIBLINEAR [17] for this purpose (Du e to the high dimen-
sionality of our feature vector, kernel-based SVM is unfeasible). W e choose L2-regularized
logistic regression (primal) with probabilistic output.
Finally, to improve the accuracy, we trained multiple models and combin ed them by
multiplying together the probabilistic output to obtained the ﬁnal pr obability distribution
over class label.
3 Data sets and Experimental Protocol
We conduct our experiment on Caltech-101 [22]. The data set consis ts of images from 101
object categories, and contains from 31 to 800 images per catego ry. Most images are medium
resolution, about 300 × 300 pixels. The signiﬁcance of this database is its large inter-class
variability. Following standard procedures, the Caltech-101 data is split into 30 training im-
ages (chosen randomly) per category and 50 for testing. BACKRO UND
GOOGLE category
was not included in the experiment. The classiﬁcation process is repe ated 4 times (changing
the training and test sets) and the average performance score is reported.
4 Results
We study the inﬂuence of a variable visual vocabulary size ( N = {1000,900, ...,100}) or
ﬁxed visual vocabulary size ( N = 1000) on several models that was then averaged together
to obtain a ﬁnal result. Note that each model has a diﬀerent visual vocabulary due to the
fact that visual word is obtained with randomly chosen exemplars. T o speedup experiment,
SIFT is used in this case.
As shown in Tab. 1, variable visual word seems to performed slightly b etter. This makes
sense, when combining several models together, models that are d iﬀerent from each other
tends to performed better after being combined.
We then experiment with diﬀerent step size and subwindow size. For m ethod A, we set
the width and height of the subwindow to be half of the original image’s width and height
respectively, while step size x and y to be the half of the subwindow’s w idth and height
respectively. For method B, we set the width and height of the subw indow to be one-third
of the original image’s width and height respectively, while step size x a nd y to be the
one-third of the subwindow’s width and height respectively. As one m ight exect, method B
would result in features with higher dimension compared to method A, due to the fact that
method B generates more regions. To speedup experiment, SIFT is used in this case.
As shown in Tab. 2, method B seems to be performing better, with alm ost negligible
increase in training time due to the higher dimension feature vector g enerated. Interestingly,
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Number of models Fixed visual vocabulary size Variable visual vocabulary size
1 71.13% 70.92%
2 73.18% 72.95%
3 73.37% 72.70%
4 73.67% 72.97%
5 73.61% 73.69%
6 73.65% 73.57%
7 73.67% 73.54%
8 73.80% 73.71%
9 73.69% 73.48%
10 73.78% 73.84%
Table 1: Table compares the accuracy using variable visual vocabula ry size vs ﬁxed visual
vocabulary size.
Number of models Method A Method B
1 70.92% 72.78%
2 72.95% 73.95%
3 72.70% 74.65%
4 72.97% 74.90%
5 73.69% 75.03%
6 73.57% 75.45%
7 73.54% 75.30%
8 73.71% 75.64%
9 73.48% 75.41%
10 73.84% 75.47%
Table 2: Table compares the accuracy using smaller subwindow size vs larger subwindow
size.
it also seems to show there is diminishing returns with averaging more m odels. Once more
than 8 models are averaged together, accuracy drops slightly.
Next, we determine the best performing local keypoint descriptor s. We test SIFT, HSV-
SIFT, Opponent-SIFT and Transformed Color SIFT.
As shown in Tab. 3, color variants of SIFT seems to be performed sig niﬁcantly better
than SIFT, indicating that color information is a pretty strong discr iminating factor.
Finally, we investigate if combining more models would improve the accur acy even fur-
ther. In method C, we combine 10 models and set visual vocabulary s ize to Eq. 1, while in
method D, we combine 15 models and set visual vocabulary size to Eq. 2. In both cases, we
used Transformed Color Sift as the local keypoint descriptor.
N = 1000 ,1000,800,800, ...,200,200 (1)
N = 1000 ,1000,1000,800,800,800, ...,200,200,200 (2)
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Number of models SIFT HSV-SIFT Opponent-SIFT Transformed color SIFT
1 72.27% 74.22% 78.18% 79.03%
2 73.97% 76.64% 79.7% 81.38%
3 74.90% 77.44% 80.72% 82.44%
4 75.20% 77.89% 81.1% 82.93%
5 75.32% 77.91% 81.70% 82.99%
6 75.15% 78.01% 82.10% 82.93%
7 75.49% 78.20% 81.9% 82.76%
8 75.66% 78.88% 81.99% 82.95%
9 75.62% 78.86% 82.27% 83.12%
10 75.85% 78.94% 82.44% 83.14%
Table 3: Table compares the accuracy using diﬀerent local keypoint descriptors.
Method C Method D
83.13% 83.46%
Table 4: Table compares the accuracy using diﬀerent number of mod els.
As shown in Tab. 4, perhaps unsurprisingly, combining more models did help to im-
prove the accuracy. We tried increasing the number more, unfort unately, we hit a point of
diminishing returns.
On a ﬁnal note, the reason having more models help obtained a higher accuracy, is
most probably due to the fact that since visual words are obtained randomly, each model’s
feature was encoded with a diﬀerent set of visual word, thereby g iving each model a diﬀerent
viewpoint of the problem.
In terms of training time, the test program which was coded in C++ an d speed up with
OpenMP to take advantage of all 4 cores, took about 5 hours to tr ain for the 10-model
classiﬁer and about 8 hours for the 15-model classiﬁer on a single qu ad-core Intel Core
i5-2500K machine.
5 Conclusion & Future works
To conclude, we have presented our proposed visual object class iﬁcation method that utilizes
only a single best-performing local keypoint descriptor (Transfor med color SIFT) while being
eﬃcient to train. We managed to obtained state of the art result on Caltech-101 of 83.46%,
showing the potential of such architecture. We suspect better r esults might be possible if
multiple models utilizes diﬀerent type local keypoint descriptor. Howe ver, it is unclear yet
if signiﬁcantly better results would be possible or if it would slow down tr aining too much.
References
[1] J. Yang, Y. Li., Y. Tian, L. Duan and W. Gao. Group sensitive multiple kernel learning
for object categorization. In ICCV, 2009
5
arXiv: some other text goes here
[2] S. Todorovic and N. Ahuja. Learning subcategory relevances f or category recognition.
In CVPR, 2008
[3] F. Li, J. Carreira and C. Sminchisescu. Object Recognition as Ran king Holistic Figure-
ground Hypotheses. In CVPR, 2010
[4] A. Bosch, A. Zisserman and X. Munoz. Image classiﬁcation using r andom forests and
ferns. In ICCV, 2007
[5] S. McCann and D. G. Lowe. Spatially Local Coding for Object Reco gnition. In ACCV,
2012
[6] Gabriel L. Oliveira, Erickson R. Nascimento, Antonio W. Vieira and M ario F. M. Cam-
pos. Sparse spatial coding: A novel approach for eﬃcient and acc urate object recogni-
tion. In ICRA, 2012
[7] O. Boiman, E. Shechtman and M. Irani. In defense of nearest-n eighbor based image
classiﬁcation. In CVPR, 2008
[8] P. Gehler and S. Nowozin. On Feature Combination for Multiclass Ob ject Classiﬁcation.
In ICCV, 2009
[9] A. Bosch, A. Zisserman and X. Munoz. Representing shape with a spatial pyramid
kernel. In CIVR, 2007
[10] K. Chatﬁeld, V. Lempitsky, A. Vedaldi and A. Zisserman. The de vil is in the details:
an evaluation of recent feature encoding method. In BMVC, 2011
[11] L. Bo, K. Lai, X. Ren and D. Fox. Object recognition with hierarc hical kernel descrip-
tors. In CVPR, 2011
[12] L. Bo, X. Ren and D. Fox. Kernel Descriptors for Visual Recog nition. In NIPS, 2010
[13] Y. Boureau, F. Bach, Y. LeCun and J. Ponce. Learning mid-leve l features for recogni-
tion. In CVPR, 2010
[14] T. Jia, C. Huang and T. Darrell. Beyond spatial pyramids: Recep tive ﬁeld learning for
pooled image features. In CVPR, 2012
[15] Koen E. A. van de Sande, Theo Gevers and Cees G. M. Snoek. Ev aluating Color De-
scriptors for Object and Scene Recognition. In PAMI, 2010
[16] Adam Coates, Honglak Lee and Andrew Ng. An analysis of single-la yer networks in
unsupervised feature learning. In NIPS, 2010
[17] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang and C.-J. Lin. LIB LINEAR: A library
for large linear classiﬁcation. In JMLR, 2008.
[18] D. C. Ciresan, U. Meier and J. Schmidhuber. Multi-column deep ne ural networks for
image classiﬁcation. In CVPR, 2012
6
arXiv: some other text goes here
[19] Y. LeCun, L. Bottou, Y. Bengio and P. Haﬀner. Gradient-base d learning applied to
document recognition. In Proceedings of the IEEE, 86(11):22782 324, November 1998
[20] P. J. Grother. NIST special database 19 - Handprinted forms and characters database.
Technical report, National Institute of Standards and Thechno logy (NIST), 1995
[21] A. Krizhevsky. Learning multiple layers of features from tiny ima ges. Masters thesis,
Computer Science Department, University of Toronto, 2009
[22] L. Fei-Fei, R. Fergus and P. Perona. Learning generative visua l models from few training
examples: An incremental bayesian approach tested on 101 objec t categories. In IEEE
CVPR Workshop of Generative Model Based Vision, 2004
[23] D. G. Lowe. Object recognition from local scale-invariant feat ures. In ICCV, 1999
[24] A. C. Berg, T. L. Berg and J. Malik. Shape matching and object r ecognition using low
distortion correspondences. In CVPR, 2005
[25] E. Shechtman and M. Irani. Matching local self-similarities acros s images and videos.
In CVPR, 2007
[26] K. Alsabti, S. Ranka and V. Singh. An eﬃcient k-means clustering algorithm. In Pro-
ceedings of the First Workshop on High Performance Data Mining, Or lando, FL, 1998
[27] H. Cao, T. Naito and Y. Ninomiya. Approximate RBF kernel SVM an d its applications
in pedestrian classiﬁcation. In 1st International Workshop on Mac hine Learning for
Vision-based Motion Analysis, 2008.
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Peer review:
[Review]:
This paper replaces the spatial pyramidal pooling in a spatial pyramid pooling by a sliding-window style pooling.
By using this method and color SIFT descriptors, state-of-the-art results are obtained on the Caltech-101 dataset (83.5% accuracy).

The contribution in this paper would be rather slight as is, but this is all the more true since it seems the idea of using sliding window pooling has already appeared in an older paper, with good results (they call the sliding windows 'components'):

'A Boosting Sparsity Constrained Bi-Linear Model for Object  Recognition'
IEEE Multimedia 2012
Chunjie Zhang  Jing Liu ;  Qi Tian ;  Yanjun Han ;  Hanqing Lu ;  Songde Ma

 Simply using it with color SIFT descriptors does not constitute enough novelty for accepting this paper.