Retrieval of trademark images by means of size functions

Cerri, Andrea; Ferri, Massimo; Giorgi, Daniela

doi:10.1016/j.gmod.2006.07.001

Cited by 54 publications

(33 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their original formulation, size functions have been widely studied and applied to Pattern Recognition problems [4,7,13,24,25]. Over the years, similar ideas have been re-proposed by Persistent Homology according to a homological approach and have found applications in shape description and data simplification [14,15].…”

mentioning

confidence: 99%

A new algorithm for computing the 2-dimensional matching distance between size functions

Biasotti

Cerri

Frosini

et al. 2011

Pattern Recognition Letters

View full text Add to dashboard Cite

Size Theory has proven to be a useful geometrical/topological approach to shape analysis and comparison. Originally introduced by considering 1-dimensional properties of shapes, described by means of real-valued functions, it has been subsequently generalized to take into account multidimensional properties coded by functions valued in R k .In the context of Size Theory, this generalization has led to introduce a shape descriptor called k-dimensional size function, and a distance to compare size functions, namely the k-dimensional matching distance. This paper proposes a novel computational framework to deal with the 2-dimensional case of Size Theory. More precisely, some new theoretical results about approximating the 2-dimensional matching distance are presented, leading to the formulation of an algorithm for its computation (up to an arbitrary error threshold).

show abstract

mentioning

confidence: 99%

A new algorithm for computing the 2-dimensional matching distance between size functions

Biasotti

Cerri

Frosini

et al. 2011

Pattern Recognition Letters

View full text Add to dashboard Cite

show abstract

“…Hussain and Eakins [8] used the topological properties of the self organizing map for similarity retrieval from a trademark image database. Cerri et al [9] utilized geometrical topological tools for describing trademark shapes and matching their similarity based on size functions of the trademarks. Jiang et al [10] have given a new approach by using the adaptive selection of visual features with different kinds of visual saliencies, including symmetry, continuity, proximity, parallelism and closure property of the trademark images.…”

Section: Existing Techniques For Trademark Retrievalmentioning

confidence: 99%

A Comprehensive Study on Content Based Trademark Retrieval System

Kumar¹,

Tripathi²,

Tiwari³

2011

IJCA

View full text Add to dashboard Cite

World is emerging with trade practices in the global scenario moving mainly towards the brands both for the products and service equally. Most of the Companies are trying to establish the brand name in the market progressively for the global recognition. As a result, Trademarks play today a very critical and important role. Every Company or Financial organization wants a distinguished trademark for brand name and market viability. Trademark registration and its evaluation for distinctiveness is thus becoming very tedious job for registration offices. Millions of trademarks already registered and millions of applications filed for trademarks registration are aggravating the problem of issuing the trademark certificates. There are different techniques and approaches currently in use for distinctness check for trademarks. The present paper gives the overview of the most popular and appreciated image processing techniques and approaches for the trademark distinctness check. Content Based Image retrieval techniques are widely used for that purpose and some other approaches like shape and texture based similarity finding techniques are also used. In the present paper, our approach is to summarize the most widely used techniques for the trademark distinctness check.

show abstract

“…They are best suited to catch qualitative features in a quantitative way: Their application is particularly useful when no standard, geometric templates are available and when the intrinsic metric between shapes is either unknown or not completely clear. Examples of applications are recognition of tree-leaves, hand-drawn sketches, monograms, white blood cells, the sign alphabet [15], [10] and retrieval of trade-marks [6] and 3D shapes [3]. A SF actually condenses the behaviour of a measuring function in a function defined on the half-plane x < y with values in the natural numbers.…”

Section: A Possible Tool For Keypics Retrieval: Size Functionsmentioning

confidence: 99%

Keypics: Free–hand Drawn Iconic Keywords

Cerri

Ferri

Frosini

et al. 2007

Int. J. Shape Model.

View full text Add to dashboard Cite

Abstract-We propose an iconic indexing of images to be exposed on the Web. This should be accomplished by "Keypics", i.e. auxiliary, simplified pictures referring to the geometrical and/or the semantic content of the indexed image.Keypics should not be rigidly standardized; they should be left free to evolve, to express nuances and to stress details. A mathematical tool for dealing with such freedom, in the retrieval task, already exists: Size Functions.An experiment on 494 Keypics with Size Functions based on three measuring functions (distances, projections and jumps) and their combination is presented.

show abstract

Retrieval of trademark images by means of size functions

Cited by 54 publications

References 29 publications

A new algorithm for computing the 2-dimensional matching distance between size functions

A new algorithm for computing the 2-dimensional matching distance between size functions

A Comprehensive Study on Content Based Trademark Retrieval System

Keypics: Free–hand Drawn Iconic Keywords

Contact Info

Product

Resources

About