A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization
Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature learning approaches have been successfully explored as alternatives for producing more representative visual features. In this work, we combine...
Saved in:
Published in | arXiv.org |
---|---|
Main Authors | , , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
20.11.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature learning approaches have been successfully explored as alternatives for producing more representative visual features. In this work, we combine both research venues, focusing on the color quantization problem. We propose two data-driven approaches to learn image representations through the search for optimized quantization schemes, which lead to more effective feature extraction algorithms and compact representations. Our strategy employs Genetic Algorithm, a soft-computing apparatus successfully utilized in Information-retrieval-related optimization problems. We hypothesize that changing the quantization affects the quality of image description approaches, leading to effective and efficient representations. We evaluate our approaches in content-based image retrieval tasks, considering eight well-known datasets with different visual properties. Results indicate that the approach focused on representation effectiveness outperformed baselines in all tested scenarios. The other approach, which also considers the size of created representations, produced competitive results keeping or even reducing the dimensionality of feature vectors up to 25%. |
---|---|
AbstractList | Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature learning approaches have been successfully explored as alternatives for producing more representative visual features. In this work, we combine both research venues, focusing on the color quantization problem. We propose two data-driven approaches to learn image representations through the search for optimized quantization schemes, which lead to more effective feature extraction algorithms and compact representations. Our strategy employs Genetic Algorithm, a soft-computing apparatus successfully utilized in Information-retrieval-related optimization problems. We hypothesize that changing the quantization affects the quality of image description approaches, leading to effective and efficient representations. We evaluate our approaches in content-based image retrieval tasks, considering eight well-known datasets with different visual properties. Results indicate that the approach focused on representation effectiveness outperformed baselines in all tested scenarios. The other approach, which also considers the size of created representations, produced competitive results keeping or even reducing the dimensionality of feature vectors up to 25%. |
Author | da S Torres, Ricardo dos Santos, Jefersson A Pereira, Érico M |
Author_xml | – sequence: 1 givenname: Érico surname: Pereira middlename: M fullname: Pereira, Érico M – sequence: 2 givenname: Ricardo surname: da S Torres fullname: da S Torres, Ricardo – sequence: 3 givenname: Jefersson surname: dos Santos middlename: A fullname: dos Santos, Jefersson A |
BookMark | eNqNzLsKwjAYhuEgClbtPQSchTSxh7UUT-CiOLiVUP6mKe2fmqSLV28RL8DpG96Hb0XmaBBmJOBCRLtsz_mShM61jDGepDyORUCeOT0BgtcVzTtlrPZNT_NhsEZWDa2NpZdeKrjDYMEBeum1QXoFaVGjor6xZlQNLUw30dso0ev312zIopadg_C3a7I9Hh7FeTddv0ZwvmzNaHFKJWdpkkVRKoT4T30AJptEEA |
ContentType | Paper |
Copyright | 2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection (Proquest) (PQ_SDU_P3) ProQuest Engineering Collection Engineering Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic Engineering Collection |
DatabaseTitleList | Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 2331-8422 |
Genre | Working Paper/Pre-Print |
GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
ID | FETCH-proquest_journals_20768117333 |
IEDL.DBID | BENPR |
IngestDate | Thu Oct 10 16:06:14 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-proquest_journals_20768117333 |
OpenAccessLink | https://www.proquest.com/docview/2076811733?pq-origsite=%requestingapplication% |
PQID | 2076811733 |
PQPubID | 2050157 |
ParticipantIDs | proquest_journals_2076811733 |
PublicationCentury | 2000 |
PublicationDate | 20201120 |
PublicationDateYYYYMMDD | 2020-11-20 |
PublicationDate_xml | – month: 11 year: 2020 text: 20201120 day: 20 |
PublicationDecade | 2020 |
PublicationPlace | Ithaca |
PublicationPlace_xml | – name: Ithaca |
PublicationTitle | arXiv.org |
PublicationYear | 2020 |
Publisher | Cornell University Library, arXiv.org |
Publisher_xml | – name: Cornell University Library, arXiv.org |
SSID | ssj0002672553 |
Score | 3.3056839 |
SecondaryResourceType | preprint |
Snippet | Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature... |
SourceID | proquest |
SourceType | Aggregation Database |
SubjectTerms | Algorithms Color Feature extraction Genetic algorithms Image management Image retrieval Measurement Optimization Representations |
Title | A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization |
URI | https://www.proquest.com/docview/2076811733 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LS8NAEB5sg-DNJz5qWdBroNmNm-1JoiRWoaUWhdxKslnrwdbapFd_uzNhqgehtw0b8phkXt-8AK5dzxlUbKXfz6OCRphp3zhcaQoxRQVq3JAKhYcjPXgNn7KbjAG3itMqNzKxEdTlpyWMnJAQTUWRSt0uv3yaGkXRVR6h0QJPBiGFab27ZDSe_KIsUkdoM6t_grbRHuk-eON86VYHsOMWh7DbJF3a6giyWFDXZ_x0Iv6Y4c3q97mIucm3QGtSPM6R3SdNsirXCC0Ed0SdCR6xI9D3x1Of10gjLqo8hqs0ebkf-JvHmfIvU03_XlCdQBt9f3cKwuVKmrdAu7JAVrPaSNvLbRmiXZGjsd8_g862K51v376APUnuYxAgs3SgXa_W7hJ1bF10oWXShy6TE4-G38kPr2iHpw |
link.rule.ids | 786,790,12792,21416,33406,33777,43633,43838 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfR1NS8Mw9KEbojc_cTo1oNfC2nRpdpIiq51uQ2VCbyVN4zy4Odfu__teyPQw2C2QkO_3_QVwZzpGImErvZ6KCiphJjxpsCXIxBQVSHFDChQejUX6Hj5l3cwp3CrnVrnGiRZRl9-adOSkCREUFMn5_eLHo6pRZF11JTR2oRlywemfy-TxT8cSiAg5Zr6BZi3tSA6h-aIWZnkEO2Z-DHvW5VJXJ5DFjHI-48Ox-GuKS9WfMxa7FN8MeUk2mCGwv1lXVRchNGcuH-qUuQI7DCV_HPq6whtyIZWncJv0Jw-pt95O7j5Mlf8fj59BAyV_cw7MKB7ID1-YskBA00IGuqN0GSJXoZDV77WgvW2mi-3dN7CfTkbDfDgYP1_CQUCCpO8j2LShUS9X5gqpbV1c2yv9BYSdhxc |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Genetic+Algorithm+Approach+for+ImageRepresentation+Learning+through+Color+Quantization&rft.jtitle=arXiv.org&rft.au=Pereira%2C+%C3%89rico+M&rft.au=da+S+Torres%2C+Ricardo&rft.au=dos+Santos%2C+Jefersson+A&rft.date=2020-11-20&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422 |