Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types Article Swipe
Punit Soni
,
Mandeep Singh
,
Purushottam Sharma
,
Tajinder Kumar
,
Xiaochun Cheng
,
Rajender Kumar
,
Mrinal Paliwal
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/jece/3889925
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/jece/3889925
There is a growing trend for using content‐based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale‐invariant feature transform (SIFT), speeded‐up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature‐based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel‐1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/jece/3889925
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/jece/3889925
- OA Status
- gold
- Cited By
- 1
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410479865
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410479865Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/jece/3889925Digital Object Identifier
- Title
-
Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data TypesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Punit Soni, Mandeep Singh, Purushottam Sharma, Tajinder Kumar, Xiaochun Cheng, Rajender Kumar, Mrinal PaliwalList of authors in order
- Landing page
-
https://doi.org/10.1155/jece/3889925Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/jece/3889925Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/jece/3889925Direct OA link when available
- Concepts
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Content (measure theory), Fusion, Computer science, Information retrieval, Content-based image retrieval, Computer vision, Image (mathematics), Image retrieval, Mathematics, Mathematical analysis, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.2500 | 211 |
| abstract_inverted_index.CBIR | 28, 32, 176, 319 |
| abstract_inverted_index.HOG, | 248, 281 |
| abstract_inverted_index.This | 223 |
| abstract_inverted_index.days | 13 |
| abstract_inverted_index.done | 49 |
| abstract_inverted_index.each | 203 |
| abstract_inverted_index.form | 108 |
| abstract_inverted_index.into | 200 |
| abstract_inverted_index.play | 69 |
| abstract_inverted_index.role | 73 |
| abstract_inverted_index.such | 160 |
| abstract_inverted_index.that | 142, 261 |
| abstract_inverted_index.this | 90, 96, 118 |
| abstract_inverted_index.used | 81, 218 |
| abstract_inverted_index.very | 71 |
| abstract_inverted_index.were | 179, 253 |
| abstract_inverted_index.will | 34, 47 |
| abstract_inverted_index.with | 181, 202, 226, 245 |
| abstract_inverted_index.(HOG) | 133 |
| abstract_inverted_index.77.2% | 289 |
| abstract_inverted_index.77.9% | 282 |
| abstract_inverted_index.84.2% | 293 |
| abstract_inverted_index.SIFT, | 246, 284 |
| abstract_inverted_index.SURF, | 247, 288 |
| abstract_inverted_index.Since | 67 |
| abstract_inverted_index.There | 0 |
| abstract_inverted_index.These | 137, 305 |
| abstract_inverted_index.about | 145 |
| abstract_inverted_index.along | 244 |
| abstract_inverted_index.goal. | 87 |
| abstract_inverted_index.image | 8, 236 |
| abstract_inverted_index.mind, | 92 |
| abstract_inverted_index.paper | 97 |
| abstract_inverted_index.shape | 147 |
| abstract_inverted_index.these | 12 |
| abstract_inverted_index.three | 103 |
| abstract_inverted_index.train | 220 |
| abstract_inverted_index.trend | 4 |
| abstract_inverted_index.usage | 183, 263 |
| abstract_inverted_index.using | 6 |
| abstract_inverted_index.where | 102 |
| abstract_inverted_index.(CBIR) | 10 |
| abstract_inverted_index.10,000 | 193 |
| abstract_inverted_index.80.02% | 279 |
| abstract_inverted_index.MATLAB | 186 |
| abstract_inverted_index.across | 213, 303 |
| abstract_inverted_index.aligns | 225 |
| abstract_inverted_index.assess | 169 |
| abstract_inverted_index.attain | 84 |
| abstract_inverted_index.basis, | 43 |
| abstract_inverted_index.common | 140 |
| abstract_inverted_index.degree | 271 |
| abstract_inverted_index.depend | 35 |
| abstract_inverted_index.fusion | 100 |
| abstract_inverted_index.images | 194, 212 |
| abstract_inverted_index.nature | 63 |
| abstract_inverted_index.object | 150 |
| abstract_inverted_index.retain | 89 |
| abstract_inverted_index.robust | 125 |
| abstract_inverted_index.subset | 209 |
| abstract_inverted_index.turned | 199 |
| abstract_inverted_index.within | 317 |
| abstract_inverted_index.(SIFT), | 123 |
| abstract_inverted_index.(SURF), | 127 |
| abstract_inverted_index.ability | 25 |
| abstract_inverted_index.because | 14 |
| abstract_inverted_index.classes | 216 |
| abstract_inverted_index.deliver | 143 |
| abstract_inverted_index.digital | 21 |
| abstract_inverted_index.display | 260 |
| abstract_inverted_index.enhance | 61 |
| abstract_inverted_index.feature | 38, 58, 110, 121 |
| abstract_inverted_index.forward | 54 |
| abstract_inverted_index.growing | 3, 18 |
| abstract_inverted_index.images. | 207 |
| abstract_inverted_index.improve | 112 |
| abstract_inverted_index.perform | 30 |
| abstract_inverted_index.present | 94 |
| abstract_inverted_index.process | 33, 40 |
| abstract_inverted_index.results | 259, 300 |
| abstract_inverted_index.system, | 101 |
| abstract_inverted_index.system. | 66, 222 |
| abstract_inverted_index.systems | 11 |
| abstract_inverted_index.various | 55, 77 |
| abstract_inverted_index.Numerous | 51 |
| abstract_inverted_index.adopted. | 136, 167 |
| abstract_inverted_index.applied, | 201 |
| abstract_inverted_index.classes, | 198 |
| abstract_inverted_index.content. | 22 |
| abstract_inverted_index.dataset, | 190 |
| abstract_inverted_index.distance | 158, 165, 267, 299, 312 |
| abstract_inverted_index.features | 68, 78, 104, 126, 134, 138, 141 |
| abstract_inverted_index.findings | 306 |
| abstract_inverted_index.interest | 19 |
| abstract_inverted_index.matching | 153, 159 |
| abstract_inverted_index.oriented | 131 |
| abstract_inverted_index.proposed | 174 |
| abstract_inverted_index.purpose, | 119 |
| abstract_inverted_index.research | 224 |
| abstract_inverted_index.semantic | 197, 215 |
| abstract_inverted_index.systems. | 240 |
| abstract_inverted_index.texture, | 249, 291 |
| abstract_inverted_index.Euclidean | 162, 273, 298 |
| abstract_inverted_index.approach, | 177 |
| abstract_inverted_index.conducted | 180 |
| abstract_inverted_index.distance, | 274 |
| abstract_inverted_index.enhancing | 75, 314 |
| abstract_inverted_index.extracted | 254 |
| abstract_inverted_index.gradients | 132 |
| abstract_inverted_index.histogram | 129 |
| abstract_inverted_index.industry, | 227 |
| abstract_inverted_index.precision | 316 |
| abstract_inverted_index.purposes, | 154 |
| abstract_inverted_index.requisite | 86 |
| abstract_inverted_index.retrieval | 9, 46, 239, 256, 276, 315 |
| abstract_inverted_index.situation | 114 |
| abstract_inverted_index.transform | 122 |
| abstract_inverted_index.Hausdrauff | 164, 266, 311 |
| abstract_inverted_index.Therefore, | 23 |
| abstract_inverted_index.accuracies | 277 |
| abstract_inverted_index.consisting | 191 |
| abstract_inverted_index.constantly | 17 |
| abstract_inverted_index.containing | 205 |
| abstract_inverted_index.extraction | 39, 59 |
| abstract_inverted_index.framework. | 320 |
| abstract_inverted_index.functions. | 257 |
| abstract_inverted_index.integrated | 106 |
| abstract_inverted_index.processing | 237 |
| abstract_inverted_index.retrieval. | 116 |
| abstract_inverted_index.similarity | 270 |
| abstract_inverted_index.simulator. | 187 |
| abstract_inverted_index.surpassing | 297 |
| abstract_inverted_index.techniques | 56, 156 |
| abstract_inverted_index.underscore | 307 |
| abstract_inverted_index.1.7%–3.6% | 302 |
| abstract_inverted_index.experiments | 178 |
| abstract_inverted_index.information | 144 |
| abstract_inverted_index.innovation, | 228 |
| abstract_inverted_index.outperforms | 272 |
| abstract_inverted_index.performance | 171 |
| abstract_inverted_index.researchers | 52 |
| abstract_inverted_index.Corel‐1000 | 189 |
| abstract_inverted_index.advancements | 234 |
| abstract_inverted_index.collectively | 82 |
| abstract_inverted_index.contributing | 232 |
| abstract_inverted_index.descriptors, | 243 |
| abstract_inverted_index.magnificence | 204 |
| abstract_inverted_index.multifeature | 99 |
| abstract_inverted_index.performance, | 76 |
| abstract_inverted_index.speeded‐up | 124 |
| abstract_inverted_index.accomplishing | 275 |
| abstract_inverted_index.capabilities. | 304 |
| abstract_inverted_index.combinations, | 252, 296 |
| abstract_inverted_index.effectiveness | 309 |
| abstract_inverted_index.characteristic | 242 |
| abstract_inverted_index.infrastructure | 230 |
| abstract_inverted_index.content‐based | 7 |
| abstract_inverted_index.scale‐invariant | 120 |
| abstract_inverted_index.multicharacteristic | 251, 295 |
| abstract_inverted_index.multifeature‐based | 175 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.86741119 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |