2 research outputs found
Texture feature evaluation for segmentation of historical document images
International audienceTexture feature analysis has undergone tremendous growth in recent years. It plays an important role for the analysis of many kinds of images. More recently, the use of texture analysis techniques for historical document image segmen-tation has become a logical and relevant choice in the conditions of significant document image degradation and in the context of lacking information on the document structure such as the document model and the typographical parameters. However, previous work in the use of texture analysis for segmentation of digitized historical document images has been limited to separately test one of the well-known texture-based approaches such as autocorrelation function, Grey Level Co-occurrence Matrix (GLCM), Gabor filters, gradient, wavelets, etc. In this paper we raise the question of which texture-based method could be better suited for discriminating on the one hand graphical regions from textual ones and on the other hand for separating textual regions with different sizes and fonts. The objective of this paper is to compare some of the well-known texture-based approaches: autocorrelation function, GLCM, and Gabor filters , used in a segmentation of digitized historical document images. Texture features are briefly described and quantitative results are obtained on simplified historical document images. The achieved results are very encouraging
Content-based indexing of low resolution documents
In any multimedia presentation, the trend for attendees taking pictures of slides that
interest them during the presentation using capturing devices is gaining popularity.
To enhance the image usefulness, the images captured could be linked to image or
video database. The database can be used for the purpose of file archiving, teaching
and learning, research and knowledge management, which concern image search.
However, the above-mentioned devices include cameras or mobiles phones have low
resolution resulted from poor lighting and noise. Content-Based Image Retrieval
(CBIR) is considered among the most interesting and promising fields as far as
image search is concerned. Image search is related with finding images that are
similar for the known query image found in a given image database. This thesis
concerns with the methods used for the purpose of identifying documents that are
captured using image capturing devices. In addition, the thesis also concerns with a
technique that can be used to retrieve images from an indexed image database. Both
concerns above apply digital image processing technique. To build an indexed
structure for fast and high quality content-based retrieval of an image, some existing
representative signatures and the key indexes used have been revised. The retrieval
performance is very much relying on how the indexing is done. The retrieval
approaches that are currently in existence including making use of shape, colour and
texture features. Putting into consideration these features relative to individual
databases, the majority of retrievals approaches have poor results on low resolution
documents, consuming a lot of time and in the some cases, for the given query image,
irrelevant images are obtained. The proposed identification and indexing method in
the thesis uses a Visual Signature (VS). VS consists of the captures slides textual
layout’s graphical information, shape’s moment and spatial distribution of colour.
This approach, which is signature-based are considered for fast and efficient
matching to fulfil the needs of real-time applications. The approach also has the
capability to overcome the problem low resolution document such as noisy image,
the environment’s varying lighting conditions and complex backgrounds. We present
hierarchy indexing techniques, whose foundation are tree and clustering. K-means
clustering are used for visual features like colour since their spatial distribution give a good image’s global information. Tree indexing for extracted layout and shape
features are structured hierarchically and Euclidean distance is used to get similarity
image for CBIR. The assessment of the proposed indexing scheme is conducted
based on recall and precision, a standard CBIR retrieval performance evaluation. We
develop CBIR system and conduct various retrieval experiments with the
fundamental aim of comparing the accuracy during image retrieval. A new algorithm
that can be used with integrated visual signatures, especially in late fusion query was
introduced. The algorithm has the capability of reducing any shortcoming associated
with normalisation in initial fusion technique. Slides from conferences, lectures and
meetings presentation are used for comparing the proposed technique’s performances
with that of the existing approaches with the help of real data. This finding of the
thesis presents exciting possibilities as the CBIR systems is able to produce high
quality result even for a query, which uses low resolution documents. In the future,
the utilization of multimodal signatures, relevance feedback and artificial intelligence
technique are recommended to be used in CBIR system to further enhance the
performance