Region-based image querying

Carson, Chad; Belongie, Serge; Greenspan, Hayit; Malik, Jitendra

doi:10.1109/ivl.1997.629719

Cited by 199 publications

(102 citation statements)

References 23 publications

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“…According to the Open Geospatial Consortium (OGC) Simple Feature Specification [65], sub-symbolic (2-D) image features are (0-D) points, (1-D) lines, (2-D) polygons, multi-part polygons (strata) or, vice versa, region boundaries (edges, contours, either closed or non-closed) provided with no semantic meaning. In the literature, image plane entities are also called image-polygons, image-objects, 2-D segments, 2-D regions, patches, parcels, blobs or tokens [78][79][80], considered as inputs to intermediate-level vision known as full primal sketch [13] or perceptual grouping [33,52,80].…”

Section: Critical Review Of Biological and Artificial Vision Conceptsmentioning

confidence: 99%

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

2012

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According to existing literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA) systems and three-stage iterative geographic object-oriented image analysis (GEOOIA) systems, where GEOOIA ⊃ GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, this methodological work is split into two parts. The first part of this work provides a multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the GEOBIA/GEOOIA approaches that augments similar analyses proposed in recent years. In line with constraints stemming from human vision, this SWOT analysis promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS) image understanding system (RS-IUS), from sub-symbolic statistical model-based (inductive) image segmentation to symbolic physical model-based (deductive) image preliminary classification. Hence, a symbolic deductive pre-attentive vision first stage accomplishes image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the second part of this work a novel hybrid (combined deductive and inductive) RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a) computational theory (system design); (b) information/knowledge representation; (c) algorithm design; and OPEN ACCESSRemote Sens. 2012, 4 2695 (d) implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time Satellite Image Automatic Mapper™ (SIAM™) is selected from existing literature. To the best of these authors' knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage, to accomplish multi-scale image segmentation and multi-granularity image pre-classification simultaneously, automatically and in near real-time.

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Section: Critical Review Of Biological and Artificial Vision Conceptsmentioning

confidence: 99%

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

2012

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“…Finally, a list of similar images will be retrieved based on the image similarity criteria. Efforts have been made to extend the query by visual example to query by region selection (Chang et al, 1998;Carson et al, 1997) and sketch (Kato et al, 1992;Daoudi and Matusiak, 2000;Chans et al, 1997;Lai, 2000;Egenhofer, 1996). Users are allowed to select their "region of interest' in the image or draw their desired content.…”

Section: Towards Semantic User Querymentioning

confidence: 99%

Towards Semantic User Query: A Review

Wang¹,

Chai²

2015

Journal of Computer Science

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This paper attempts to discuss the image query mechanisms and user needs for image retrieval. The explosive growth of image data leads to the need of research and development of Image retrieval. Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users' mind. This paper also highlights both the already addressed and outstanding issues.

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“…LLVE includes a battery of low-level sub-symbolic (nonsemantic) general-purpose domain-independent inductive-learning (fine-to-coarse, bottom-up) driven-without-knowledge inherently ill-posed image processing algorithms called image segmentation for simplicity's sake (also refer to Part I Section 2.4.1.2) (Matsuyama & Shang-Shouq Hwang, 1990). As output, the image segmentation first stage provides image features, namely points and regions (segments, [2-D] objects, parcel or blobs (Carson et al, 1997;Lindeberg, 1993;Yang & Wang, 2007), see Part I Section 2.3) or, vice versa, region boundaries, i.e., edges, provided with no semantic meaning (see Part I Section 2.4.1.2). 3.…”

Section: (3-d)mentioning

confidence: 99%

Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part II

Baraldi¹

2012

Earth Observation

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Region-based image querying

Abstract: Retrieving images from large and varied collections using image content as a key is

Cited by 199 publications

References 23 publications

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Towards Semantic User Query: A Review

Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part II

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