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Compressively Sensed Image Recognition

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 7th European Workshop on Visual Information Processing (EUVIP)
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)978-1-5386-6897-9
ISBN (painettu)978-1-5386-6898-6
DOI - pysyväislinkit
TilaJulkaistu - marraskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2471-8963

Conference

ConferenceEUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING
Ajanjakso1/01/00 → …

Tiivistelmä

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudorandom measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.

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