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008			150903s20132013caua fob 000 0 eng d
020			_a9781608459186 _qpaperback _cRM175.20
039		9	_a201601111730 _basrul _c201512301139 _dbaiti _c201512070908 _drahah _y09-03-2015 _zrahah
040			_aCaBNvSL _cJ2I _dJ2I _dWAU _dYDXCP _dNST _dAZS _dE7B _dUMI _dDEBSZ _dCOO _dUKM
090			_aTA1637.L894 3
090			_aTA1637 _b. L894 3
100			_aLux, Mathias. _eauthor
245	1	0	_aVisual information retrieval using Java and LIRE _h[electronic resource] / _cMathias Lux, Oge Marques.
264		1	_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2013.
264		4	_c©2013.
300			_a1 online resource (xv, 96 p.) : _bill.
490	1		_aSynthesis lectures on information concepts, retrieval, and services, _x1947-9468 ; _v#25
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 87-94).
505	0		_aPreface -- Acknowledgments --
505	8		_a1. Introduction -- 1.1 Design challenges -- 1.2 Getting started with LIRE -- 1.2.1 Java setup -- 1.2.2 Downloading, unpacking, and running LireDemo -- 1.2.3 Indexing an image collection -- 1.2.4 Browsing the index, selecting an image, and performing a search --
505	8		_a2. Information retrieval: selected concepts and techniques -- 2.1 Basic concepts and document representation -- 2.1.1 Vector retrieval model -- 2.2 Retrieval evaluation -- 2.3 Text information retrieval with Lucene --
505	8		_a3. Visual features -- 3.1 Digital imaging in a nutshell -- 3.1.1 Digital imaging in Java -- 3.2 Global features -- 3.2.1 Color features -- 3.2.2 Texture features -- 3.2.3 Combining color and texture -- 3.3 Local features -- 3.3.1 Scale-invariant feature transform (SIFT) -- 3.3.2 Speeded-up robust features (SURF) -- 3.4 Metrics, normalization, and distance functions -- 3.5 Evaluation of visual features -- 3.5.1 Figures of merit -- 3.5.2 Datasets -- 3.5.3 Challenges -- 3.6 Feature extraction using LIRE --
505	8		_a4. Indexing visual features -- 4.1 Indexing: the na詶e approach -- 4.1.1 Basic indexing and linear search in LIRE -- 4.2 Nearest-neighbor search -- 4.3 Hashing -- 4.3.1 Locality sensitive hashing -- 4.3.2 Metric spaces approximate indexing -- 4.4 Bag of visual words -- 4.4.1 Bag of visual words using LIRE --
505	8		_a5. LIRE: an extensible Java CBIR library -- 5.1 Architecture and low-level features -- 5.2 Indexing and searching -- 5.3 Advanced features -- 5.3.1 Bag of visual words -- 5.3.2 Result re-ranking and filtering -- 5.4 How to apply LIRE -- 5.4.1 Scenario investigation -- 5.4.2 Benchmarking -- 5.4.3 Deployment tests and performance optimization --
505	8		_a6. Concluding remarks -- 6.1 Research directions, challenges, and opportunities -- 6.2 Resources -- Bibliography -- Authors' biographies.
520	3		_aVisual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) from large, unstructured repositories. The goal of VIR is to retrieve matches ranked by their relevance to a given query, which is often expressed as an example image and/or a series of keywords. During its early years (1995-2000), the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the lack of coincidence between an image's visual contents and its semantic interpretation, also known as semantic gap, required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this introductory book, we focus on a subset of VIR problems where the media consists of images, and the indexing and retrieval methods are based on the pixel contents of those images--an approach known as content-based image retrieval (CBIR). We present an implementation-oriented overview of CBIR concepts, techniques, algorithms, and figures of merit. Most chapters are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for CBIR.
588			_aDescription based on online resource; title from PDF t.p. (Morgan & Claypool, viewed on February 17, 2013).
650		0	_aPicture archiving and communication systems.
650		0	_aImage processing _xDigital techniques. _959990
650		0	_aJava (Computer program language) _960204
650		0	_aLucene Image REtrieval.
700	1		_aMarques, Oge. _eauthor
830		0	_aSynthesis lectures on information concepts, retrieval, and services, _x1947-9468 ; _v#25
907			_a.b16202399 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kTA1637.L894 3
914			_avtls003592728
990			_abety
991			_aFakulti Sains Teknologi dan Maklumat
998			_al _b2015-03-09 _cm _da _feng _gcau _y0 _z.b16202399
999			_c597979 _d597979