Latent semantic analysis-based image auto annotation

Bakalem, Mahdia; Benblidia, Nadjia; Oukid, Saliha

doi:10.1109/icmwi.2010.5648152

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Scientific Programming texts with a conceptual content [18]. Furthermore, the LSA method has also been used to retrieve images with quite promising results [17,19,20,21]. However, the computational complexity and the memory requirements of the LSA method have been shown to be extremely high over the years [22][23][24], especially when its Singular Value Decomposition (SVD) process is considered.…”

Section: Related Workmentioning

confidence: 99%

Ship-SIBISCaS: A First Step towards the Identification of Potential Maritime Law Infringements by means of LSA-Based Image

Leon-Paredes

Barbosa-Santillán

Sánchez-Escobar³

et al. 2019

Scientific Programming

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Maritime safety and security are being constantly jeopardized. Therefore, identifying maritime flow irregularities (semi-)automatically may be crucial to ensure maritime security in the future. This paper presents a Ship Semantic Information-Based, Image Similarity Calculation System (Ship-SIBISCaS), which constitutes a first step towards the automatic identification of this kind of maritime irregularities. In particular, the main goal of Ship-SIBISCaS is to automatically identify the type of ship depicted in a given image (such as abandoned, cargo, container, hospital, passenger, pirate, submersible, three-decker, or warship) and, thus, classify it accordingly. This classification is achieved in Ship-SIBISCaS by finding out the similarity of the ship image and/or description with other ship images and descriptions included in its knowledge base. This similarity is calculated by means of an LSA algorithm implementation that is run on a parallel architecture consisting of CPUs and GPUs (i.e., a heterogeneous system). This implementation of the LSA algorithm has been trained with a collection of texts, extracted from Wikipedia, that associate some semantic information to ImageNet ship images. Thanks to its parallel architecture, the indexing process of this image retrieval system has been accelerated 10 times (for the 1261 ships included in ImageNet). The range of the precision of the image similarity method is 46% to 92% with 100% recall (that is, a 100% coverage of the database).

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Section: Related Workmentioning

confidence: 99%

Ship-SIBISCaS: A First Step towards the Identification of Potential Maritime Law Infringements by means of LSA-Based Image

Leon-Paredes

Barbosa-Santillán

Sánchez-Escobar³

et al. 2019

Scientific Programming

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“… AnnotB-LSA algorithm in latent space [13] [14]. The latent semantic analysis (LSA) model [15] is used by the algorithm in order to extract the latent semantic relationships in the space of the textual key words and to minimize the ambiguity (polysemy, synonymy) between the key words that annotate the blobs.…”

Section: B New Image Processing Stepmentioning

confidence: 99%

A comparative image auto-annotation

Bakalem

Benblidia

Ait-Aoudia

2013

IEEE International Symposium on Signal Processing and Information Technology

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The image annotation is an effective technology for improving the Web image retrieval. Many works have been proposed to increase the image auto-annotation performance. The annotation quality depends on many factors: segmentation, choice of visual features...etc.. An image contains several visuals information (color, texture, shape….) but the best choice of these parameters is a difficult problem. The main focus of this paper is two-fold. First, we compare between image annotations in latent space and textual space. Second, we survey influence of visual features choice on image annotation. For that, we developed two image auto-annotation systems (AIA-LSA, AIA-WLSA). For each one, we propose three prototypes based on different visual descriptors; such as: texture, color and fusion of both parameters.Corel data set is used to experiment our prototypes, the results show that the annotation by latent space is more efficient than the annotation by textual space and the best choice of visual features is a persistent problem in general images.

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Latent semantic analysis-based image auto annotation

Cited by 2 publications

References 6 publications

Ship-SIBISCaS: A First Step towards the Identification of Potential Maritime Law Infringements by means of LSA-Based Image

Ship-SIBISCaS: A First Step towards the Identification of Potential Maritime Law Infringements by means of LSA-Based Image

A comparative image auto-annotation

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