Multi-scale gist feature manifold for building recognition

Zhao, Cairong; Liu, Chuancai; Lai, Zhihui

doi:10.1016/j.neucom.2011.03.035

Cited by 30 publications

(12 citation statements)

References 32 publications

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“…The spatial structure of the image is presented by naturalness, roughness, ruggedness, openness, and expansion as perceptual dimensions. The Gist descriptor has been used for other applications, such as scene detection and classification in the literature [16,17,25]. The Gist descriptor simply uses low-level features to compute higher-level semantic properties and usually consists of the semantic label and the spatial layout of images.…”

Section: Gist Descriptormentioning

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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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: Gist Descriptormentioning

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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“…This recognition scheme was proved as invariant against linear transformation like rotation, translation and scaling. Zhao, Liu and Lai [6] proposed multi-scale generalized index search tree (GIST) feature manifold for building recognition. They used energy spectral information that represents the spatial frequencies spread everywhere in the image.…”

Section: Related Workmentioning

confidence: 99%

Baggage Recognition in Occluded Environment using Boosting Technique

2017

KSII TIIS

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Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

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“…Gist descriptor [13,14] and wavelet transform [16], as two appearance-based texture features, have traditionally been used in the field of computer vision and image processing for natural scene image analysis. In [12], Gist features were used for detection and classification of the target in satellite images.…”

Section: Introductionmentioning

confidence: 99%

“…To capture the finer details of scene image, the effectiveness of angular partitioning was considered. Multi-scale Gist features were used to represent the structural information of the buildings in the image for building recognition [16]. The wavelet transform as a texture-based feature has been used for different applications of signal and image analysis [17].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Gist Operator for Document Image Retrieval

Alaei

Pal

et al. 2018

2018 13th IAPR International Workshop on Document Analysis Systems (DAS)

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As digitised documents normally contain a large variety of structures, a page segmentation-and layout-free method for document image retrieval is preferable. In this research work, therefore, wavelet transform as a transformbased approach is initially used to provide different undersampled images from the original image. Then, Gist operator, as a feature extraction technique, is employed to extract a set of global features from the original image as well as the sub-images obtained from the wavelet transform. Moreover, the column-wise variances of the values in each sub-image are computed and they are then concatenated to obtain another set of features. Considering each feature set, locality-sensitive hashing is employed to compute similarity distances between a query and the document images in the database. Finally, a classifier fusion technique using the mean function is taken into account to provide a document image retrieval result. The combination of these features and a clustering score fusion strategy provides higher document image retrieval accuracy. Two different databases of the document image are considered for experimentation. The results obtained from the experimental study are detailed and the results are encouraging.

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Multi-scale gist feature manifold for building recognition

Cited by 30 publications

References 32 publications

Document Image Retrieval Based on Visual Saliency Maps

Document Image Retrieval Based on Visual Saliency Maps

Baggage Recognition in Occluded Environment using Boosting Technique

Evaluation of Gist Operator for Document Image Retrieval

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