Information retrieval from visual databases using multiple representations and multiple queries

Abbadeni, Noureddine

doi:10.1145/1529282.1529625

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…Against this background, it was suggested to combine the results of several uni-modal queries [16]. Yet, fusing search results of different queries neglects that the relevance ranking obtained for different modalities might be incompatible.…”

Section: Motivationmentioning

confidence: 99%

“…This, however, introduces a potential source of errors, so that multi-modal IR should not depend on such reconciliation. Moreover, the chosen embedding technique shall be invariant to certain transformations of heterogeneous data, such as rotation and illumination for images or watermarking and time-scale modification for audio and video data [16], [22], [23].…”

Section: Design Principlesmentioning

confidence: 99%

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Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Duong

Nguyen

Yin

et al. 2022

IEEE Trans. Knowl. Data Eng.

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The heterogeneity of today's Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user's information needs by different data modalities, such as keywords, hashtags, user profiles, and other media.Recent IR systems answer such a multi-modal query by considering it as a set of separate uni-modal queries. However, depending on the chosen operationalisation, such an approach is inefficient or ineffective. It either requires multiple passes over the data or leads to inaccuracies since the relations between data modalities are neglected in the relevance assessment. To mitigate these challenges, we present an IR system that has been designed to answer genuine multi-modal queries. It relies on a heterogeneous network embedding, so that features from diverse modalities can be incorporated when representing both, a query and the data over which it shall be evaluated. By embedding a query and the data in the same vector space, the relations across modalities are made explicit and exploited for more accurate query evaluation. At the same time, multi-modal queries are answered with a single pass over the data. An experimental evaluation using diverse real-world and synthetic datasets illustrates that our approach returns twice the amount of relevant information compared to baseline techniques, while scaling to large multi-modal databases.Index Terms-query embedding, graph embedding, heterogeneous information network ✦ • A model for heterogeneous data: We introduce a representation of heterogeneous data based on HINs to capture the semantic relations between data of different modalities of the same entity [13], [14]. This includes a

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Section: Motivationmentioning

confidence: 99%

Section: Design Principlesmentioning

confidence: 99%

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Duong

Nguyen

Yin

et al. 2022

IEEE Trans. Knowl. Data Eng.

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“…Texture refers to the spatial allotment of grey-levels and can be defined as the deterministic or random repetition of one or several primitives in an image. Micro textures refer to textures with small primitives while Macro textures refer to textures with large primitives [11], [12]. Texture analysis technique has been used in several domains like classification, segmentation and shape from texture and image retrieval.…”

Section: Texturementioning

confidence: 99%

“…The important features that has considered in this study are coarseness, contrast, directionality & busyness. Following are conceptual definitions of all these features [3] [6] [11].…”

Section: A Perceptual Textural Featuresmentioning

confidence: 99%

Experimental analysis of perceptual based texture features for image retrieval

Hassekar

Sawant²

2015

2015 International Conference on Communication, Information &Amp; Computing Technology (ICCICT)

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The perception-based approach of feature extraction methods for CBIR has been summarized. It has proposed an experimental analysis of mathematical modeling of textural contents for images, having a perceptual meaning and application such as coarseness, directionality, contrast, and busyness. An objective is to find an effective method to estimate perceptual features. So a cumulative use of computational intelligence to build a Knowledge Based Expert system for dental related diseases is proposed.

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Computational Perceptual Features for Texture Representation and Retrieval

Abbadeni

2011

IEEE Trans. on Image Process.

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A perception-based approach to content-based image representation and retrieval is proposed in this paper. We consider textured images and propose to model their textural content by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, and busyness. The proposed computational measures can be based upon two representations: the original images representation and the autocorrelation function (associated with original images) representation. The set of computational measures proposed is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results and benchmarking show interesting performance of our approach. First, the correspondence of the proposed computational measures to human judgments is shown using a psychometric method based upon the Spearman rank-correlation coefficient. Second, the application of the proposed computational measures in texture retrieval shows interesting results, especially when using results fusion returned by each of the two representations. Comparison is also given with related works and show excellent performance of our approach compared to related approaches on both sides: correspondence of the proposed computational measures with human judgments as well as the retrieval effectiveness.

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Information retrieval from visual databases using multiple representations and multiple queries

Cited by 5 publications

References 27 publications

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Experimental analysis of perceptual based texture features for image retrieval

Computational Perceptual Features for Texture Representation and Retrieval

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