Curvature Scale Space Based Trade Mark Recognition

Curvature Scale Space Based Trade Mark Recognition 

1 S.China Venkateswarlu, 2 G.Nagendra, 3 S.RamaSubba Reddy, 4 G.Soma Sekhar 

1 Professor & HOD-CSE/ECE, HITS College of Engg.,, Hyderabad 

2 Assistant Professor –ECE-VJIT, Hyderabad 

3 Assistant Professor –ECE-VJIT, Hyderabad 

4 Research Scholar –Acharya Nagarju University,Guntur 

Abstract— Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval 

system. The efficient retrieval of the data base image is dependent on various factors such as the environment considered 

for query image, the reading source and the channel for communication. These factors affect the recognition of any given 

query image. To overcome the effect of environmental noises and rotation effect a new image recognition algorithm based 

on curvature scale space (CSS) is proposed. Database retrieval method based on shape information using curvature feature 

is proposed. The method evaluates the closed counter and process on the curvature smoothening using different level of 

Gaussian values. The proposed CSS based recognition algorithm found to be efficient in recognition for invariant features 

such as rotation and noise level. The proposed task is to be implemented on Matlab tool using the image processing toolbox 

for it’s realization.. 

Keywords— Audio, Curvature Scale Space, Database, Image Retrieval, Speech, Processors 

I. INTRODUCTION 

Information is inherently multimodal. Humans can 

efficiently and effectively process information 

simultaneously in multiple dimensions. These multiple 

media, that aid effective communication, can be 

characterized into speech, audio, image, video, and 

textual data. Advances in computing and networking are 

generating a significant amount of interest in multimedia 

services and applications. Powerful processors, highspeed 

networking, high-capacity storage devices, 

improvements in compression algorithms, and advances 

in processing of audio, speech, image, and video signals 

are making multimedia systems technically and 

economically feasible. 

Multimedia systems suggest a wide variety of 

potential applications such as interactive entertainment, 

video news distribution, video rental services, and digital 

multimedia libraries. These services aim to provide the 

user with on-demand multimedia services. Any system 

providing these services will have to address the issue of 

representation, indexing, retrieval, and manipulation of 

the multimedia data. The multimedia objects are 

archived in a database. A feature extraction module 

extracts various features from the database, and 

represents the objects in terms of these features. The user 

interface provides the user with the capabilities to query, 

extract features, and insert multimedia objects. 

Image and video are an integral part of multimedia 

data. There are a number of applications where images 

need to be automatically retrieved by their Connell. This 

necessitates the need for powerful image processing and 

understanding tools. Various applications in digital 

libraries and image databases have been described in the 

literature. 

II. IMAGE RETRIEVAL SYSTEM 

Digital images are a convenient media for 

describing and storing spatial, temporal, spectral, and 

physical components of information contained in a 

variety of domains (e.g.. aerial/satellite images in remote 

sensing, medical images in telemedicine. fingerprints in 

forensics, museum collections in art history, and 

registration of trademarks and logos). These databases 

typically consist of thousands of images, taking up gigabytes 

of memory space. 

While advances in image compression algorithms 

have alleviated the storage requirement to some extent, 

the large volume of these images makes it difficult for a 

user to browse through the entire database. Therefore, an 

efficient and automatic procedure is required for 

indexing and retrieving images from databases. 

Figure 1: Traditional image retrieval model 

The proposed hierarchical content-based image 

retrieval algorithm is applied to a trademark image 1 

database. 1 with 1,100 images (See Appendix A for 

representative images belonging to this database). 

In the fast pruning stage, simple and easily 

calculated shape' features, including edge direction 

histograms and invariant moments are used to quickly 

browse' the- database for a small set of plausible 

matches. The retrieved images are then screened using a 

more elaborate, but costly deformable template matching 

process to remove false retrievals. 

III. METHODS USING A SINGLE CUE 

Traditional image retrieval systems use a single cue 

such as shape, texture, or color to represent the image 

and retrieval is based on the features that represent the 

chosen cue. Although color seems to be a highly reliable 

attribute for image retrieval, situations where color 

Published in International Journal of Advanced Engineering & Application, Jan 2011 Issue 225

information is not present in the images require the use 

of shape and/or texture attributes for image retrieval. 

Retrieval based on a single image attribute might 

lack sufficient discriminatory information and might not 

be able to accommodate large scale and orientation 

changes. For example, color-based approaches cannot 

distinguish between a red apple and a red Ferrari. 

Additional shape information can very easily distinguish 

the two. 

IV. TRADE MARK RECOGNITION 

Trademarks represent a gamut of pictorial data. 

There are over a million registered trademarks in the 

U.S. alone, and they represent a number of goods and 

products, which are sold by different manufacturers and 

service and other organizations. Most of the trademarks 

are an abstract representation of a concept in the world, 

like an abstract drawing of an animal, or a natural object 

(Sun. Moon, etc.). It is extremely challenging and 

instructive to study and address the issue of image 

database retrieval on this huge source of pictorial data. A 

trademark is either a word, phrase, symbol or design, or 

combination of words, phrases, symbols or designs, 

which identifies and distinguishes the source of goods or 

services of one party from those of others. A service 

mark is the same as a trademark except this it identifies 

and distinguishes the source of a service rather than a 

product. Thus, while a trademark appears on the product 

or it‘s packaging, the service mark appears in advertising 

for the services. 

V. TRADEMARK REGISTRATION 

Trademarks in the U.S. are registered with the 

USPTO (U.S. Patent and Trademark Office). An 

applicant may apply for federal registration in three 

principle ways. An applicant can process a use 

application if the applicant has already commenced the 

use of the mark in products or services. This application 

is filed on the basis of the use of the mark. If the 

applicant has not yet used the mark but intends to use it 

in the future, the applicant can file an intent-to-use 

application on the basis of a bona fide intention to use 

the mark. The third option allows international 

applicants to apply for a mark that has already been 

applied or registered in another country. Thus, an 

applicant from outside the United States may file within 

the United States. 

VI. SEARCHES FOR CONFLICTING MARKS 

Before a mark is registered with the USPTO, an 

examining attorney conducts a search for conflicting 

marks. Usually, it is not necessary for an applicant to 

conduct a search for conflicting marks prior to filing an 

application. The application fee covers processing and 

search costs, and is not refunded in case a conflict is 

found and the mark cannot be registered. 

VII INVARIENT-MOMENT BASED METHOD 

Retrieval speed and accuracy are two main issues in 

designing image databases. System accuracy can be 

defined in terms of precision and recall rates. A 

precision rate can be defined as the percent of retrieved 

images similar to the query among the total number of 

retrieved images. A recall rate is defined as the percent 

of retrieved images which are similar to the query 

among the total number of images similar to the query in 

the database. It can be easily seen that both precision and 

recall rates are a function of the total number of retrieved 

images. In order to have a high accuracy, the system 

needs to have both a high precision and a high recall 

rate. Although, simple image features can be easily 

extracted, they lack sufficient expressiveness and 

discriminatory information to determine if two images 

have a similar content. Thus, there exists a trade-off 

between speed and accuracy. In order to build a system 

with both high speed and accuracy, we use a hierarchical 

two-level feature extraction and matching structure for 

image retrieval. Our system uses multiple shape features 

for the initial pruning stage. Retrievals based on these 

features are integrated for better accuracy and higher 

system recall rate. The second stage uses deformable 

template matching to eliminate the false retrievals 

present at the output of the first stage, thereby improving 

the precision rate of the system. 

VIII. CURVATURE SCALE SPACE (CSS) METHOD 

A useful general – purpose shape representation 

method in computational vision should make accurate 

and reliable recognition of an object possible. Therefore, 

such a representation should necessarily satisfy a 

number of criteria. The following is a list of such 

criteria. Note that when two planar curves are described 

as having the same shape, there exists a transformation 

consisting of uniform scaling, rotation, and translation, 

which will cause one of those curves to overlap the 

other. 

A. Invariance: If two curves have the same shape, they 

should also have the same representation. Uniqueness: 

If two curves have the same shape, they should also have 

the different representation. 

B .Stability: If two curves have a small difference, their 

representations should also have a small difference, and 

if two representations have small difference, the curves 

they represent should also have small shape difference. 

The importance of the invariance criterion is that it 

guarantees that all curves with the same shape will have 

the same representation. It will therefore be possible to 

conclude that two curves have different shapes by 

observing that they have different representations. 

Without the invariance criterion, two curves with the 

same shape may have different representations. The 

uniqueness criterion is important since it guarantees 

C. Efficiency: The representation should be efficient to 

compute and store. This is important since it may 

necessary for an object recognition system to perform 

real-time recognition. By efficient, we mean that the 

computational complexity should be a low-order 

polynomial in time and space (and in the number of 

processors if a parallel computing architecture is used) 

as a function of the size of the input curve. 

D. Ease of implementation: If two or more competing 

representations exist, it is advantageous to choose one of 


those representations such that the implementation of the 

computer program that computes that representation 

requires the least time spent on programming and 

debugging. 

E. Computation of shape properties: It may be useful to 

be able to determine properties of the shape of a curve 

using its representation. For example if a curve has a 

symmetric shape, it may be desirable to be able to 

determine that fact from its representation (the symmetry 

criterion). Furthermore, if the shape of a whole curve or 

part of a curve is the same as the shape of part of another 

curve, it may be useful to be able to determine that 

relationship using their representations (the part/whole 

criterion). 

IX. THE CURVATURE SCALE SPACE IMAGE 

A planar curve is a set of points whose position 

vectors are the values of a continuous, vector-valued 

function. It can be represented by the parametric vector 

equation 

r (u) = (x (u), y(u)) (1) 

The function r (u) is a parametric representation of the 

curve. Planar curve has an infinite number of distinct 

parametric representations. A parametric representation 

in which the parameter is the arc length s is called a 

natural parameterization of the curve. A natural 

parameterization can be computed from an arbitrary 

parameterization using the following equation: 

(2) 

Where ŕ represents the derivative.i.e, ŕ = dr/dv. For any 

parameterization 

(3) 

Where t(u) and n(u) are the tangent and normal vectors 

at u, respectively. For any planar curve, the vectors t(u) 

and n(u) must satisfy the simplified serret-Frenet vector 

equations: 

Where k(s) is the curvature of the curve at s and is 

defined as 

Where Ф is the angle between t(s) and t (s+h). Now, 

observe that 

t(s) =dt/ds =(dt/du) (du/ds) 

Therefore 

dt/du = (ds / du ) kn =│ ŕ │kn 

Hence 

Differentiating the expression for t (u), we obtain 

It now follows that 

Published in International Journal of Advanced Engineering & Application, Jan 2011 Issue 227 

(4) 

X. CORNER DETECTION 

The algorithm of edge and boundary interpretation 

is based on operating with object's vertices, where each 

vertex is a corner with the line segments that are 

connected to it. Each vertex of the object according to 

the definition is located in corner. This is the reason we 

first needed to detect all the corners of the object. Corner 

detection is an important task in various computer vision 

and image-understanding systems [1]. Corner detection 

should satisfy a number of important criteria: 

_ All the true corners should be detected 

_ No false corners should be detected 

_ Corner points should be well localized 

_ Corner detector should be robust with respect to noise 

_ Corner detector should be efficient 

There are different methods for corner detection in 

literature. Each one is based on different principle. Each 

method addresses different problem in corner detection. 

For example, [2] uses a generalized Hough transform for 

edge detection, where the transform is needed to detect 

the edge lines (boundaries) of the object and the 

generalization is performed to cope with the corners that 

are not sharp. On the other hand analyses the curvature 

scale of object's contour and extracts the points with the 

maxima of absolute curvature. The method is based on 

CSS (The Curvature Scale-Space Technique) method. 

XI. CURVATURE SCALE SPACE (CSS) 

The CSS technique is suitable fro recovering 

invariant geometric features (curvature zero crossing 

points and/or extrema) of a planar curve at multiple 

scales. To compute it, the curve G is first parameterized 

by the arc length parameter u: 

(5) 

An evolved version Gs of G can then be computed. 

Where 

(6) 

(7) 

Where is the convolution operator and 

denotes a Gaussian of width . In order to find 

curvature zero – crossings or extrema from evolved 

versions of the input curve, one needs to compute 

curvature: 

Where, 

(8) 

(9)

The corners are defined as the local maxima of the 

absolute value of curvature. At a very fine scale, there 

exist many such maxima due to noise and the digital 

contour. As the scale is increased, the noise is smoothed 

away and only the maxima corresponding to the real 

corners remain. The CSS corner-detection method finds 

the corners at these local maxima 

The process of CSS image corner detection is as follows: 

Utilize the canny edge detector to extract edges 

from the original image. 

Extract the edge contours from the image: 

― Fill the gaps in the edge contours. 

― Find the T – junctions and mark them as T – 

corners. 

Compute the curvature at highest scale σhigh 

and determine the corner candidates by 

comparing the neighboring minima. 

Track the corners to the lowest scale to improve 

localization. 

Compare the T – corners to the corners found 

using the curvature procedure and remove 

corners which are very close. 

The following is an explanation of each stage of 

the CSS corner detector. 

Steps description: 

Here canny edge detection was used, but it may be 

replaced by any other edge detector 

The canny edge detector can cause gaps at T-junctions 

and the corners may not be found with the CSS method. 

― If the endpoint is nearly connected to another 

endpoint, fill the gap and continue the 

extraction 

―If the endpoint is nearly connected to an edge contour, 

but not to another endpoint, 

mark this point as a T-junction corner. 

Figure 2: Two cases of gaps in the edge contours 

. 

Figure 3: case where one corner is marked twice 

XII. DATABASE 

The image database used in this implementation was 

created by the collection of large number of trademarks 

consisting of 1001 images collected from different 

sources. A design mark is registered in the binary form 

at the USPTO. The trademarks are converted from gray 

level images to binary images. 

Figure 4: Image retrieval model 

The trademarks in our database were selected so 

that many of them have similar perceptual meaning to 

make the retrieval problem more challenging. These 

trademarks encompass a wide variety of objects; some 

are based on the alphabets of the English language, 

while others represent the Sun, Earth, humans, eyes, 

animals, etc. 

XIII. RESULT ANALYSIS 

Figure 5:Input interface for query reading 


Figure 6: Menu option for selection of operation 

XIV. CSS BASED METHOD: 

Case-I 

Image Taken: 

Orientation: ‗0 Degree‘ 

Noise level: 0 

Figure 7: Original Query image taken 

Figure 8: Taken image for testing the Implementation 

Figure 9: Extracted counter for given Query Image 

Figure 10: Curvature Scale space (CSS) plot for the given query image 

Figure 11: Classified Images for Given Query Image 

Figure 12: Recognized image based on CSS method 

CASE-II 

Image Taken: 


Noise level: 0 


Figure 14: Taken image at 30 degree for testing the Implementation 


Figure 15: Extracted Counter for given Query Image 




CASE-IV 

Image Taken: 


Noise Level: mean = 0.1, Variance = 0.2. 

Noise Type: Gaussian 


Figure 20: Taken noisy image for testing the Implementation 

Figure 21: Extracted Counter for given Query Image 





XV. CONCLUSION 

An efficient shape-based retrieval algorithm has 

been developed to retrieve trademark images. Efficiency 

and accuracy of retrievals are achieved by designing a 

two stage hierarchical retrieval system. The proposed 

CSS based method shows promising technique for shape 

–based image database retrieval. The technique is robust 

under rotated, scaled, and noisy versions of the database 

images. The CSS based method is compared with the 

invariant based estimation algorithm for the evaluation 

of accuracy in estimation and classification. It is 

observed that the CSS based estimation outperforms the 

estimation with less classified symbols with accurate 

retreveation of trademark image as compared to the 

Invariant based method. 

The proposed pruning strategy is robust under noisy 

(hand drawn trademark images and faded master-marks), 

ar-bitrarily oriented (master-mark images) and scaled 

images (multiple trademarks in database with different 

scales as well as scale difference in master-marks in 

database with respect to real marks extracted from the 

bottom of the tankards). 

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