Document Image Retrieval Based on Texture Features: A Recognition-Free Approach

Alaei, Fahimeh; Alaei, Alireza; Pal, Umapada; Blumenstein, Michael

doi:10.1109/dicta.2016.7797033

Cited by 5 publications

(12 citation statements)

References 33 publications

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“…To have a fast and effective retrieval system, a fast measuring distance is necessary. In the previous studies, the k-nearest neighbor (k-NN) method has usually been used to find the similarity distance between a query image and trained document images [10,11]. Since we have a large dataset, locality-sensitive hashing (LSH) [13] is taken into account in this work.…”

Section: Similarity Matchingmentioning

confidence: 99%

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Document Image Retrieval Based on Visual Saliency Maps

Alaei

Pal

et al. 2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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There has been a massive growth in the production of various unstructured, complex and multilingual digitised documents in recent years. Storing and manipulating such digitised documents towards a paperless society has been the objective of emerging technology. As the human visual system can easily distinguish the global summary of images, extracting features based on human attention from images is desirable to achieve more accurate document image retrieval results. Thus, in this research work, an appearance-based document image retrieval system using image saliency maps depending on human visual attention is proposed. The saliency map obtained from the input document image is used to generate a weighted document image. Features are then extracted from the weighted document images using the Gist operator. Then, locality-sensitive hashing is considered to compute similarity distances between a query and the document images in the knowledge-based database. To evaluate the performance of the proposed document image retrieval system MTDB, ITESOFT, and CLEF-IP datasets of document images were used for experimentation. The proposed document image retrieval system provided promising retrieval results compared to the results reported in the literature.

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Section: Similarity Matchingmentioning

confidence: 99%

“…Texture feature analysis has played a significant role in different research areas, such as medical imaging, industrial inspection, and remote sensing [9,10]. A repetitive pattern of information or arrangement of a structure with orderly intervals is defined as a texture.…”

Section: Introductionmentioning

confidence: 99%

Document Image Retrieval Based on Visual Saliency Maps

Alaei

Pal

et al. 2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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“…Several modified versions of LBP have also been undertaken to overcome the drawbacks of the LBP method and to enhance the performance of different systems [1,4,19,20]. The applicability of various LBP-based feature extraction methods has also been shown in different applications [7,11,12,[15][16][17].…”

Section: Proposed Fast Local Binary Patternmentioning

confidence: 99%

“…Various feature extraction techniques have also been developed for document image analysis and retrieval [8][9][10]. The advantages and beneficial uses of texture features for document image retrieval have further been discussed in [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Fast local binary pattern: Application to document image retrieval

Alaei

Pal

et al. 2017

2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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The volume of digitised documents is increasing every day. Thus, designing a fast document image retrieval method for the large volume of document images, especially when the document images are also large in size, is of high demand. As feature extraction is one of the important steps in every document image retrieval system, a feature extraction technique with a low computing time and small feature number has a direct effect on the speed of the retrieval system. In this paper, we propose a non-parametric texture feature extraction method based on summarising the local grey-level structure of the image. To extract the proposed features, the input image is, at first, divided into a set of overlapping patches of equal size. The peripheral pixels of the centre pixel in a patch are used to extract two sets of patterns. The patterns are derived from the vertical & horizontal, and diagonal & off-diagonal pixels of the patch, separately. From each set of pixels, 15 (=2^4-1) different local binary patterns are extracted in our proposed feature extraction method. Two histograms of the local binary patterns obtained from these two sets of pixels are then created and concatenated to obtain 30 features called fast local binary patterns (F-LBP). To evaluate the efficiency of the proposed feature extraction method, MTDB and ITESOFT databases were considered for experimentation. The proposed F-LBP provided promising results with lower computing time as well as smaller memory space consumption.

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