Modified one-class support vector machine for content-based image retrieval with relevance feedback Article Swipe

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Oluwole Abiodun Adegbola , David Aborisade , Segun I. Popoola , Abraham Amole , Aderemi A. Atayero ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1080/23311916.2018.1541702

Image retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Example CBIR (QVER) system. The inherent curse of dimensionality associated with RF mechanism was catered for by performing feature selection using Principal Component Analysis (PCA). The amount of feature dimension retained was determined based on a not more than 5% loss constrain imposed on average precision of retrieval result. While the asymmetry and small sample size nature of the resultant image dataset informed the use of a modified One-Class Support Vector Machine (OC-SVM) classifier, three image databases (DB10, DB20 and DB100) were used to test the OC-SVM RF mechanism. Across DB10, DB20 and DB100, Average Indexing Time of 0.451, 0.3017, and 0.0904s were recorded, respectively. For a critical recall value of 0.3, precision values for QVER were 0.7881, 0.7200 and 0.9112, while OC-SVM RF yielded precision of 0.8908, 0.8409, and 0.9503, respectively. Also, the use of PCA yielded tolerable degradation of 3.54, 4.39 and 7.40% in precision on DB10, DB20, and DB100, respectively, with 80% reduction in feature dimension. The OC-SVM RF increased the precision and invariably the reliability of the CBIR system by ranking most of the relevant images higher. Also, the target class was identified faster than the conventional method, thereby reducing the image retrieval time of the OC-SVM RF.

Related Topics

Support Vector Machine

Artificial Intelligence

Content-Based Image Retrieval

Computer Science

Principal Component Analysis

Feature Selection

Concepts

Support vector machine Artificial intelligence Content-based image retrieval Image retrieval Computer science Relevance feedback Pattern recognition (psychology) Dimensionality reduction Principal component analysis Semantic gap Precision and recall Feature selection Image (mathematics)

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Type: article
Language: en
Landing Page: https://doi.org/10.1080/23311916.2018.1541702
OA Status: gold
Cited By: 2
References: 25
Related Works: 10
OpenAlex ID: https://openalex.org/W2899290156

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DOI: https://doi.org/10.1080/23311916.2018.1541702

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Title: Modified one-class support vector machine for content-based image retrieval with relevance feedback

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Type: article

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Language: en

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Publication year: 2018

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Publication date: 2018-01-01

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Authors: Oluwole Abiodun Adegbola, David Aborisade, Segun I. Popoola, Abraham Amole, Aderemi A. Atayero

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Landing page: https://doi.org/10.1080/23311916.2018.1541702

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Concepts: Support vector machine, Artificial intelligence, Content-based image retrieval, Image retrieval, Computer science, Relevance feedback, Pattern recognition (psychology), Dimensionality reduction, Principal component analysis, Semantic gap, Precision and recall, Feature selection, Image (mathematics)

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Cited by: 2

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abstract_inverted_index.RF	50, 126, 162, 200
abstract_inverted_index.by	37, 55, 212
abstract_inverted_index.in	184, 195
abstract_inverted_index.of	14, 22, 46, 66, 85, 96, 104, 136, 149, 165, 174, 179, 208, 215, 237
abstract_inverted_index.on	73, 82, 186
abstract_inverted_index.to	122
abstract_inverted_index.80%	193
abstract_inverted_index.For	144
abstract_inverted_index.PCA	175
abstract_inverted_index.RF.	240
abstract_inverted_index.The	43, 64, 198
abstract_inverted_index.and	91, 118, 131, 139, 158, 168, 182, 189, 204
abstract_inverted_index.for	54, 153
abstract_inverted_index.gap	12
abstract_inverted_index.not	75
abstract_inverted_index.the	10, 89, 97, 102, 124, 172, 202, 206, 209, 216, 221, 228, 233, 238
abstract_inverted_index.use	103, 173
abstract_inverted_index.via	2
abstract_inverted_index.was	31, 52, 70, 224
abstract_inverted_index.(RF)	29
abstract_inverted_index.0.3,	150
abstract_inverted_index.4.39	181
abstract_inverted_index.CBIR	40, 210
abstract_inverted_index.DB20	117, 130
abstract_inverted_index.QVER	154
abstract_inverted_index.Time	135
abstract_inverted_index.into	33
abstract_inverted_index.loss	79
abstract_inverted_index.more	76
abstract_inverted_index.most	214
abstract_inverted_index.size	94
abstract_inverted_index.test	123
abstract_inverted_index.than	77, 227
abstract_inverted_index.this	25
abstract_inverted_index.time	236
abstract_inverted_index.used	121
abstract_inverted_index.were	120, 141, 155
abstract_inverted_index.with	18, 49, 192
abstract_inverted_index.3.54,	180
abstract_inverted_index.7.40%	183
abstract_inverted_index.Also,	171, 220
abstract_inverted_index.DB10,	129, 187
abstract_inverted_index.DB20,	188
abstract_inverted_index.Image	0, 5
abstract_inverted_index.Query	36
abstract_inverted_index.While	88
abstract_inverted_index.based	72
abstract_inverted_index.class	223
abstract_inverted_index.curse	45
abstract_inverted_index.human	19
abstract_inverted_index.image	15, 99, 114, 234
abstract_inverted_index.often	8
abstract_inverted_index.small	92
abstract_inverted_index.three	113
abstract_inverted_index.using	59
abstract_inverted_index.value	148
abstract_inverted_index.while	160
abstract_inverted_index.(CBIR)	7
abstract_inverted_index.(DB10,	116
abstract_inverted_index.(PCA).	63
abstract_inverted_index.(QVER)	41
abstract_inverted_index.0.451,	137
abstract_inverted_index.0.7200	157
abstract_inverted_index.Across	128
abstract_inverted_index.DB100)	119
abstract_inverted_index.DB100,	132, 190
abstract_inverted_index.OC-SVM	125, 161, 199, 239
abstract_inverted_index.Vector	109
abstract_inverted_index.Visual	38
abstract_inverted_index.amount	65
abstract_inverted_index.faster	226
abstract_inverted_index.images	218
abstract_inverted_index.incurs	9
abstract_inverted_index.nature	95
abstract_inverted_index.paper,	26
abstract_inverted_index.recall	147
abstract_inverted_index.sample	93
abstract_inverted_index.system	211
abstract_inverted_index.target	222
abstract_inverted_index.values	152
abstract_inverted_index.0.0904s	140
abstract_inverted_index.0.3017,	138
abstract_inverted_index.0.7881,	156
abstract_inverted_index.0.8409,	167
abstract_inverted_index.0.8908,	166
abstract_inverted_index.0.9112,	159
abstract_inverted_index.0.9503,	169
abstract_inverted_index.Average	133
abstract_inverted_index.Example	39
abstract_inverted_index.Machine	110
abstract_inverted_index.Support	108
abstract_inverted_index.average	83
abstract_inverted_index.catered	53
abstract_inverted_index.dataset	100
abstract_inverted_index.feature	57, 67, 196
abstract_inverted_index.higher.	219
abstract_inverted_index.images.	23
abstract_inverted_index.imposed	81
abstract_inverted_index.method,	230
abstract_inverted_index.ranking	213
abstract_inverted_index.result.	87
abstract_inverted_index.results	17
abstract_inverted_index.system.	42
abstract_inverted_index.thereby	231
abstract_inverted_index.yielded	163, 176
abstract_inverted_index.(OC-SVM)	111
abstract_inverted_index.Analysis	62
abstract_inverted_index.Feedback	28
abstract_inverted_index.Indexing	134
abstract_inverted_index.critical	146
abstract_inverted_index.informed	101
abstract_inverted_index.inherent	44
abstract_inverted_index.modified	106
abstract_inverted_index.reducing	232
abstract_inverted_index.relevant	217
abstract_inverted_index.retained	69
abstract_inverted_index.semantic	11, 20
abstract_inverted_index.Component	61
abstract_inverted_index.One-Class	107
abstract_inverted_index.Principal	60
abstract_inverted_index.Relevance	27
abstract_inverted_index.Retrieval	6
abstract_inverted_index.asymmetry	90
abstract_inverted_index.constrain	80
abstract_inverted_index.databases	115
abstract_inverted_index.dimension	68
abstract_inverted_index.increased	201
abstract_inverted_index.mechanism	30, 51
abstract_inverted_index.precision	84, 151, 164, 185, 203
abstract_inverted_index.recorded,	142
abstract_inverted_index.reduction	194
abstract_inverted_index.resultant	98
abstract_inverted_index.retrieval	1, 16, 86, 235
abstract_inverted_index.selection	58
abstract_inverted_index.tolerable	177
abstract_inverted_index.associated	48
abstract_inverted_index.determined	71
abstract_inverted_index.dimension.	197
abstract_inverted_index.identified	225
abstract_inverted_index.invariably	205
abstract_inverted_index.mechanism.	127
abstract_inverted_index.performing	56
abstract_inverted_index.classifier,	112
abstract_inverted_index.degradation	178
abstract_inverted_index.reliability	207
abstract_inverted_index.traditional	3, 35
abstract_inverted_index.conventional	229
abstract_inverted_index.incorporated	32
abstract_inverted_index.Content-Based	4
abstract_inverted_index.respectively,	191
abstract_inverted_index.respectively.	143, 170
abstract_inverted_index.dimensionality	47
abstract_inverted_index.interpretation	21
abstract_inverted_index.problem—non-correlation	13
cited_by_percentile_year.max	94
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corresponding_author_ids	https://openalex.org/A5067451508, https://openalex.org/A5057364146
countries_distinct_count	1
institutions_distinct_count	5
corresponding_institution_ids	https://openalex.org/I186771145, https://openalex.org/I45230691
citation_normalized_percentile.value	0.47194369
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False