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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1997年第3期9卷 435-447页

作者： Petrakis, EGM Faloutsos, C MULTIMEDIA SYST INST CRETE GR-73100 KHANIA GREECE UNIV MARYLAND DEPT COMP SCI COLLEGE PK MD 20742 USA

We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of ''labeled'' or ''expected'' objects (e.g., ''heart,'' ''lungs,'' etc.) are common in all images of a given application domain in addition to a variable number of ''unexpected'' or ''unlabeled'' objects (e.g.,''tumor,'' ''hematoma,'' etc.). The method can answer queries by example, such as ''find all X-rays that are similar to Smith's X-ray.'' The stored images are mapped to points in a multidimensional space and are indexed using state-of-the-art database methods (R-trees). The proposed method has several desirable properties: (a) Database search is approximate, so that all images up to a prespecified degree of similarity (tolerance) are retrieved. (b) It has no ''false dismissals'' (i.e., all images qualifying query selection criteria are retrieved). (c) It is much faster than sequential scanning for searching in the main memory and on disk (i.e., by up to an order of magnitude), thus scaling-up well for large databases.

关键词： image database image retrieval by content query by example image content representation attributed relational graph image indexing R-tree similarity searching

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Shape retrieval based on dynamic programming

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2000年第1期9卷 141-147页

作者： Milios, E Petrakis, EGM York Univ Dept Comp Sci N York ON M3J 1P3 Canada Tech Univ Crete Dept Elect & Comp Engn Chania Crete Greece

We propose a shape matching algorithm for deformed shapes based on dynamic programming. Our algorithm is capable of grouping together segments at finer scales in order to come up with appropriate correspondences with segments at coarser scales. We illustrate the effectiveness of our algorithm in retrieval of shapes by content on two different two-dimensional (2-D) datasets, one of static hand gesture shapes and another of marine life shapes. We also demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval, such as Fourier descriptors and geometric and sequential moments. Our evaluation is based on human relevance judgments following a well-established methodology from the information retrieval field.

关键词： dynamic programming image database query by example relevance judgments shape retrieval

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Scalable color image indexing and retrieval using vector wavelets

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2001年第5期13卷 851-861页

作者： Albuz, E Kocalar, E Khokhar, AA Sony Elect San Jose CA 95134 USA UltimateTV Inc Sunnyvale CA 94086 USA Univ Illinois Dept Elect & Comp Sci Chicago IL 60607 USA

This paper presents a scalable content-based image indexing and retrieval system based on vector wavelet coefficients of color images. Highly decorrelated wavelet coefficient planes are used to acquire a search efficient feature space. The feature space is subsequently indexed using properties of all the images in the database. Therefore, the feature key of an image not only corresponds to the content of the image itself but also to how much the image is different from the other images being stored in the database. The search time linearly depends on the number of images similar to the query image and is independent of the database size. We show that, in a database of 5,000 images, query search takes less than 30 msec on a 266 MHz Pentium II processor, compared to several seconds of retrieval time in the earlier systems proposed in the literature.

关键词： wavelet transform content-based image indexing query by example image retrieval scalable indexing retrieval systems

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A factor graph framework for semantic video indexing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2002年第1期12卷 40-52页

作者： Naphade, MR Kozintsev, IV Huang, TS IBM Corp Thomas J Watson Res Ctr Hawthorne NY 10532 USA Univ Illinois Dept Elect & Comp Engn Beckman Inst Adv Sci & Technol Urbana IL 61801 USA

Video query by semantic keywords is one of the most challenging research issues in video data management. To go beyond low-level similarity and access video data content by semantics, we need to bridge the gap between the low-level representation and high-level semantics. This is a difficult multimedia understanding problem. We formulate this problem as a probabilistic pattern-recognition problem for modeling semantics in terms of concepts and context. To map low-level features to high-level semantics, we propose probabilistic multimedia objects (multijects). examples of multijects in movies include explosion, mountain, beach, outdoor, music, etc. Semantic concepts in videos interact and appear in context. To model this interaction explicitly, we propose a network of multijects (multinet). To model the multinet computationally, we propose a factor graph framework which can enforce spatio-temporal constraints. Using probabilistic models for multijects, rocks, sky, snow, water-body, and forestry/greenery, and using a factor graph as the multinet, we demonstrate the application of this framework to semantic video indexing. We demonstrate how detection performance can be significantly improved using the multinet to take inter-conceptual relationships into account. Our experiments using a large video database consisting of clips from several movies and based on a set of five semantic concepts reveal a significant improvement in detection performance by over 22%. We also show how the multinet is extended to take temporal correlation into account. By constructing a dynamic multinet, we show that the detection performance is further enhanced by as much as 12%. With this framework, we show how keyword-based query and semantic filtering is possible for a predetermined set of concepts.

关键词： factor graphs hidden Markov models likelihood ratio test multimedia understanding probabilistic graphical networks probability propagation query by example query by keywords ROC curves semantic video indexing sum-product algorithm

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A Review of Deep Learning Techniques in Document Image Word Spotting

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2022年第2期29卷 1085-1106页

作者： Kumari, Lalita Sharma, Anuj Panjab Univ Dept Comp Sci & Applict Chandigarh India

From the early days of pattern recognition, word spotting have been important test beds for studying how well machines can perform better decision making. In recent years, word spotting have made dramatic advances with state-of-the-art techniques reaching high level of performance in real life applications. This word spotting domain have driven research by providing suitable yet well-defined challenges for pattern recognition and document analysis practitioners. We continue in this direction by covering extensive literature and new challenges in this domain with comparison of previous work. In particular, we have covered recent deep learning technique role in word spotting and future scope of word spotting with deep learning. We believe writing suitable review of word spotting will not only be crucial for understanding of this field in today era, but also in broader collaborative efforts, especially those with artificial intelligence based tasks. To facilitate future research in word spotting, we have discussed word spotting from learning environment, including its framework design with components as query phase, preprocessing stages, segmentation, feature extraction, feature representation and matching process strategies. Further, deep learning working and use in word spotting architecture has been discussed. The study also include an experimental comparison for the research community to evaluate algorithmic advances along with benchmarked datasets, and future challenges in this field.

关键词： Document image Word spotting Segmentation Documents indexing Convolutional neural network Deep learning Feature representation query by string query by example

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Content-based retrieval for human motion data

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2004年第3期15卷 446-466页

作者： Chiu, CY Chao, SP Wu, MY Yang, SN Lin, HC Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300 Taiwan Chang Jung Christian Univ Dept Informat Management Tainan 711 Taiwan

In this study, we propose a novel framework for constructing a content-based human motion retrieval system. Two major components, including indexing and matching, are discussed and their corresponding algorithms are presented. In indexing, we introduce an affine invariant posture feature and propose an index map structure based on the posture distribution of raw data. To avoid the curse of dimensionality, the high-dimension posture feature of the entire skeleton is decomposed into the direct sum of low-dimension segment-posture features of skeletal segments. In matching, the start and end frames of a query example are first indexed into index maps to find candidate clips from the given motion collection. Then the similarity between the query example and each candidate clip is computed through dynamic time warping. Some experimental examples are given to demonstrate the effectiveness and efficiency of proposed algorithms. (C) 2004 Elsevier Inc. All rights reserved.

关键词： Curse of Dimensionality Indexing Human motion query by example Skeletal system Content retrieval

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Hybrid negative example selection using visual and conceptual features

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MULTIMEDIA TOOLS AND APPLICATIONS 2014年第3期71卷 967-989页

作者： Shirahama, Kimiaki Matsuoka, Yuta Uehara, Kuniaki Kobe Univ Grad Sch Econ Nada Ku Kobe Hyogo 6578501 Japan Kobe Univ Grad Sch Engn Nada Ku Kobe Hyogo 6578501 Japan Kobe Univ Grad Sch Syst Informat Nada Ku Kobe Hyogo 6578501 Japan

An application of query-By-example (QBE) is presented where shots that are visually similar to provided example shots are retrieved. To implement QBE, counter-example shots are required to accurately distinguish shots that are relevant to the query from those that are not (Li and Snoek (2009), Yu et al. (2004)). However, there are usually a huge number of shots, not relevant to a particular query, which can serve as counter-example shots. It is difficult for a user to provide counter-example shots that would aid retrieval. Thus, we developed a QBE method based on partially supervised learning where a retrieval model is constructed by selecting counter-example shots from shots without user supervision. To ensure the speed and accuracy of the QBE method, we select a small number of counter-example shots that are visually similar to given example shots but irrelevant to the query. Such shots are useful for characterizing the boundary between relevant and irrelevant shots. For our method, we first filter shots that are visually dissimilar to example shots based on SVMs on a visual feature. Then we filter shots relevant to the query based on concept detection results from pre-constructed classifiers. Shots that pass the above two tests are considered as counter-example shots. Experimental results obtained using TRECVID 2009 video data validate the effectiveness of our method.

关键词： Negative example selection Partially supervised learning query by example Visual feature Conceptual feature

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Event retrieval in video archives using rough set theory and partially supervised learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2012年第1期57卷 145-173页

This paper develops a query-by-example method for retrieving shots of an event (event shots) using example shots provided by a user. The following three problems are mainly addressed. Firstly, event shots cannot be retrieved using a single model as they contain significantly different features due to varied camera techniques, settings and so forth. This is overcome by using rough set theory to extract multiple classification rules with each rule specialized to retrieve a portion of event shots. Secondly, since a user can only provide a small number of example shots, the amount of event shots retrieved by extracted rules is inevitably limited. We thus incorporate bagging and the random subspace method. Classifiers characterize significantly different event shots depending on example shots and feature dimensions. However, this can result in the potential retrieval of many unnecessary shots. Rough set theory is used to combine classifiers into rules which provide greater retrieval accuracy. Lastly, counter example shots, which are a necessity for rough set theory, are not provided by the user. Hence, a partially supervised learning method is used to collect these from shots other than example shots. Counter example shots, which are as similar to example shots as possible, are collected because they are useful for characterizing the boundary between event shots and the remaining shots. The proposed method is tested on TRECVID 2009 video data.

关键词： query by example Rough set theory Bagging Random subspace Small sample size problem Partially supervised learning

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Subgraph spotting in graph representations of comic book images

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PATTERN RECOGNITION LETTERS 2018年 112卷 118-124页

作者： Thanh Nam Le Luqman, Muhammad Muzzamil Dutta, Anjan Heroux, Pierre Rigaud, Christophe Guerin, Clement Foggia, Pasquale Burie, Jean-Christophe Ogier, Jean-Marc Llados, Josep Adam, Sebastien Univ La Rochelle L3i La Rochelle France Univ Salerno Salerno Italy Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Univ Rouen LITIS Rouen France

Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Attributed graph Region adjacency graph Graph matching Graph isomorphism Subgraph isomorphism Subgraph spotting Graph indexing Graph retrieval query by example Dataset and comic book images

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INACT-INDECT Advanced Image Cataloguing Tool

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MULTIMEDIA TOOLS AND APPLICATIONS 2014年第1期68卷 95-110页

作者： Grega, Michal Bryk, Damian Napora, Maciej AGH Univ Sci & Technol PL-30059 Krakow Poland

Child pornography possession and distribution are crimes which are prosecuted in most countries around the world. In some cases the law is so strict that even police forces are not allowed to gather and catalogue evidence for future reference. This paper presents an innovative solution to this problem. The authors present tools for cataloguing high- and low-level metadata of the evidence material. Furthermore, a tool for fast and accurate search for such evidence in suspects' file systems is proposed.

关键词： Live forensics query by example Bees Algorithm Child pornography

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