Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048673476> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W3048673476 endingPage "147" @default.
- W3048673476 startingPage "140" @default.
- W3048673476 abstract "This study focuses on the area of visual indoor place recognition (e.g., in an office setting, automatically recognizing different places, such as offices, corridor, wash room, etc.). The potential applications include robot navigation, augmented reality, and image retrieval. However, the task is extremely demanding because of the variations in appearance in such dynamic setups (e.g., view-point, occlusion, illumination, scale, etc.). Recently, Convolutional Neural Network (CNN) has emerged as a powerful learning mechanism, able to learn deep higher-level features when provided with a comparatively big quantity of labeled training data. Here, we exploit the generic nature of CNN features for robust visual place recognition in the challenging COLD dataset. So, we employ the pre-trained CNNs (on the related tasks of object and scene classification) for deep feature extraction in the COLD images. We demonstrate that these off-the-shelf features, when combined with a simple linear SVM classifier, outperform their bag-of-features counterpart. Moreover, a simple combination scheme, combining the local bag-of-features and higher-level deep CNN features, produce outstanding results on the COLD dataset." @default.
- W3048673476 created "2020-08-18" @default.
- W3048673476 creator A5000247176 @default.
- W3048673476 creator A5017651512 @default.
- W3048673476 creator A5075464273 @default.
- W3048673476 date "2020-08-04" @default.
- W3048673476 modified "2023-10-18" @default.
- W3048673476 title "Local and Deep Features for Robust Visual Indoor Place Recognition" @default.
- W3048673476 cites W1524680991 @default.
- W3048673476 cites W1993229407 @default.
- W3048673476 cites W2016053056 @default.
- W3048673476 cites W2062118960 @default.
- W3048673476 cites W2066941820 @default.
- W3048673476 cites W2102605133 @default.
- W3048673476 cites W2104126266 @default.
- W3048673476 cites W2131846894 @default.
- W3048673476 cites W2151103935 @default.
- W3048673476 cites W2155893237 @default.
- W3048673476 cites W2161969291 @default.
- W3048673476 cites W2162915993 @default.
- W3048673476 doi "https://doi.org/10.31580/ojst.v3i2.1475" @default.
- W3048673476 hasPublicationYear "2020" @default.
- W3048673476 type Work @default.
- W3048673476 sameAs 3048673476 @default.
- W3048673476 citedByCount "0" @default.
- W3048673476 crossrefType "journal-article" @default.
- W3048673476 hasAuthorship W3048673476A5000247176 @default.
- W3048673476 hasAuthorship W3048673476A5017651512 @default.
- W3048673476 hasAuthorship W3048673476A5075464273 @default.
- W3048673476 hasConcept C108583219 @default.
- W3048673476 hasConcept C12267149 @default.
- W3048673476 hasConcept C153180895 @default.
- W3048673476 hasConcept C154945302 @default.
- W3048673476 hasConcept C165696696 @default.
- W3048673476 hasConcept C31972630 @default.
- W3048673476 hasConcept C38652104 @default.
- W3048673476 hasConcept C41008148 @default.
- W3048673476 hasConcept C52622490 @default.
- W3048673476 hasConcept C64876066 @default.
- W3048673476 hasConcept C81363708 @default.
- W3048673476 hasConcept C95623464 @default.
- W3048673476 hasConceptScore W3048673476C108583219 @default.
- W3048673476 hasConceptScore W3048673476C12267149 @default.
- W3048673476 hasConceptScore W3048673476C153180895 @default.
- W3048673476 hasConceptScore W3048673476C154945302 @default.
- W3048673476 hasConceptScore W3048673476C165696696 @default.
- W3048673476 hasConceptScore W3048673476C31972630 @default.
- W3048673476 hasConceptScore W3048673476C38652104 @default.
- W3048673476 hasConceptScore W3048673476C41008148 @default.
- W3048673476 hasConceptScore W3048673476C52622490 @default.
- W3048673476 hasConceptScore W3048673476C64876066 @default.
- W3048673476 hasConceptScore W3048673476C81363708 @default.
- W3048673476 hasConceptScore W3048673476C95623464 @default.
- W3048673476 hasIssue "2" @default.
- W3048673476 hasLocation W30486734761 @default.
- W3048673476 hasOpenAccess W3048673476 @default.
- W3048673476 hasPrimaryLocation W30486734761 @default.
- W3048673476 hasRelatedWork W2733060750 @default.
- W3048673476 hasRelatedWork W2773120646 @default.
- W3048673476 hasRelatedWork W2909609880 @default.
- W3048673476 hasRelatedWork W2980720988 @default.
- W3048673476 hasRelatedWork W3046699463 @default.
- W3048673476 hasRelatedWork W3200612453 @default.
- W3048673476 hasRelatedWork W3208028783 @default.
- W3048673476 hasRelatedWork W4210880282 @default.
- W3048673476 hasRelatedWork W564581980 @default.
- W3048673476 hasRelatedWork W2187500075 @default.
- W3048673476 hasVolume "3" @default.
- W3048673476 isParatext "false" @default.
- W3048673476 isRetracted "false" @default.
- W3048673476 magId "3048673476" @default.
- W3048673476 workType "article" @default.