Logo Matching for Document Image Retrieval

Abstract

Graphics detection and recognition are fundamental research problems in document image analysis and retrieval. As one of the most pervasive graphical elements in business and government documents, logos may enable immediate identification of organizational entities and serve extensively as a declaration of a document's source and ownership. In this work, we developed an automatic logo-based document image retrieval system that handles: 1) Logo detection and segmentation by boosting a cascade of classifiers across multiple image scales; and 2) Logo matching using translation, scale, and rotation invariant shape descriptors and matching algorithms. Our approach is segmentation free and layout independent and we address logo retrieval in an unconstrained setting of 2-D feature point matching. Finally, we quantitatively evaluate the effectiveness of our approach using large collections of real-world complex document images.

Identification of Institutional Logo based on Wavelet Features

“…The fundamental idea of using discrete wavelets is to analyze the signals according to scale. It has gained a lot of attention in the area of signal processing, numerical analysis and mathematics during recent years [9] [11]. Generally, the wavelet transforms is an advanced technique used for signal and image analysis.…”

“…Their segmentation process is based on a top-down, hierarchical X-Y tree structures and they have extracted 16 statistical features from the segment and set of rules are developed using ID3 algorithm to recognize the presence of logo in unknown region. Zhu et al [9] developed a detection system which is segmentation free, fast and layout independent based on generic features and shape descriptors of the connected components and line profiles. Hongye Wang [10] has identified the deficiency of the available method lacking in adoptability to variable real-worl documents.…”

Automated Color Logo Recognition System based on Shape and Color Features

“…Various methods have been tested for their effectiveness in Logo Recognition [5] such as Log-Polar Transform (LPT), Fourier-Mellin Transform (FMT) and Gradient Location-Orientation Histogram. In [6] the authors have used Canny Edge Detector to extract object contours for recognition. Other techniques that have been used for logo recognition include Angular Radial Transform [7], radial Tchebichef moments, Zernike Moments, Legendre Moments [8].…”

Features Extraction Based on Linear Regression Technique

“…Baboulaz and Dragotti (2009) demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well-suited for modelling the acquisition of images by a camera. Simulations of image registration and image superresolution of artificially sampled images are first presented, analyzed and compared to traditional techniques In reference (Zhu and Doermann, 2009), their approach is segmentation free and layout independent and they address logo retrieval in an unconstrained setting of 2-D feature point matching. Finally, they quantitatively evaluate the effectiveness of their approach using large collections of real-world complex document images.…”