Semantic-Friendly Indexing and Quering of Images Based on the Extraction of the Objective Semantic Cues

Mojsilović, Aleksandra; Gomes, Jonas; Rogowitz, Bernice E.

doi:10.1023/b:visi.0000004833.39906.33

Cited by 57 publications

(44 citation statements)

References 40 publications

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“…We think that our image-search results compare well to theirs (e.g. figure 8 (Heidemann, 2005); figures 12-14 (Mojsilovic et al, 2004)), yet our results are obtained without the use of any color information. An important constraint of such an applied image search is that it has to occur fast.…”

Section: System Performancesupporting

confidence: 66%

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An Approach to the Parameterization of Structure for Fast Categorization

Rasche

2009

Int J Comput Vis

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A decomposition is described, which parameterizes the geometry and appearance of contours and regions of gray-scale images with the goal of fast categorization. To express the contour geometry, a contour is transformed into a local/global space, from which parameters are derived classifying its global geometry (arc, inflexion or alternating) and describing its local aspects (degree of curvature, edginess, symmetry). Regions are parameterized based on their symmetric axes, which are evolved with a wave-propagation process enabling to generate the distance map for fragmented contour images. The methodology is evaluated on three image sets, the Caltech 101 set and two sets drawn from the Corel collection. The performance nearly reaches the one of other categorization systems for unsupervised learning.

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Section: System Performancesupporting

confidence: 66%

“…(Heidemann, 2005;Wang et al, 2001;Mojsilovic et al, 2004;Vogel and Schiele, 2007). These studies use traditional techniques such as image segmentation, template matching and interest points.…”

Section: System Performancementioning

confidence: 99%

An Approach to the Parameterization of Structure for Fast Categorization

Rasche

2009

Int J Comput Vis

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“…So, the relative capacity of BAM recalling pattern pairs is 96 × 64 × 0.1998 = 1227 pairs [20]. On the other hand, according to the statement in [1] that about 40 to 240 lexicons are needed to define for perfectly detecting the concepts carried by images, the capacity of the constructed pixel-based BAM capacity is pretty sufficient in calculating the recalling values r n i , which are used to generate the associative values with given lexicons by (9), if the number of given lexicons for a domain is in the range of (40, 240).…”

Section: Calculating Associative Valuesmentioning

confidence: 99%

“…However, the user query as "find images with 10-30% of sky" is not a natural way to present the semantics of the images. In Mojsilovic et al [9], a semantic-friendly query language for searching diverse collections of images was proposed. However, same as Vogel and Schiele [8], the query language such as (nature <10 and contrast >800) is not easy to utilize for modeling the categories.…”

Section: Introductionmentioning

confidence: 99%

Representing Images' Meanings by Associative Values with Given Lexicons Considering the Semantic Tolerance Relation

Dai

2011

Advances in Multimedia

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An approach of representing meanings of images based on associative values with lexicons is proposed. For this, the semantic tolerance relation model (STRM) that reflects the tolerance degree between defined lexicons is generated, and two factors of semantic relevance (SR) and visual similarity (VS) are involved in generating associative values. Furthermore, the algorithm of calculating associative values using pixel-based bidirectional associative memories (BAMs) in combination with the STRM, which is easy in implementation, is depicted. The experiment results of multilexicons-based retrieval by individuals show the effectiveness and efficiency of our proposed method in finding the expected images and the improvement in retrieving accuracy because of incorporating SR with VS in representing meanings of images.

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“…However, humans use semantic concepts to identify and describe colors [16]. Hence, we adopt both numeric features directly extracted from the image such as Region_Average_Color and semantic features like Region_Color_Name mapped from the visual features as described in [20].…”

Section: B Feature Extraction and Analysismentioning

confidence: 99%

Semantic, Automatic Image Annotation Based on Multi-Layered Active Contours and Decision Trees

Olszewska¹

2013

IJACSA

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Abstract-In this paper, we propose a new approach for automatic image annotation (AIA) in order to automatically and efficiently assign linguistic concepts to visual data such as digital images, based on both numeric and semantic features. The presented method first computes multi-layered active contours. The first-layer active contour corresponds to the main object or foreground, while the next-layers active contours delineate the object's subparts. Then, visual features are extracted within the regions segmented by these active contours and are mapped into semantic notions. Next, decision trees are trained based on these attributes, and the image is semantically annotated using the resulting decision rules. Experiments carried out on several standards datasets have demonstrated the reliability and the computational effectiveness of our AIA system.

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Semantic-Friendly Indexing and Quering of Images Based on the Extraction of the Objective Semantic Cues

Cited by 57 publications

References 40 publications

An Approach to the Parameterization of Structure for Fast Categorization

An Approach to the Parameterization of Structure for Fast Categorization

Representing Images' Meanings by Associative Values with Given Lexicons Considering the Semantic Tolerance Relation

Semantic, Automatic Image Annotation Based on Multi-Layered Active Contours and Decision Trees

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