Sensor Network for the Monitoring of Ecosystem: Bird Species Recognition

Cai, Jinhai; Ee, Dominic; Pham, Binh; Roe, Paul; Zhang, Jinglan

doi:10.1109/issnip.2007.4496859

Cited by 94 publications

(78 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…", a lot of work has been done on the topic [46,9,69,62,1,68,38,20,17] and it is still attracting much research today, in particular on deep learning techniques. In parallel to the emergence of automated identification tools, large social networks dedicated to the production, sharing and identification of multimedia biodiversity records have increased in recent years.…”

Section: Lifeclef Lab Overviewmentioning

confidence: 99%

“…Using audio records rather than bird pictures is justified by current practices [9,69,68,8]. Birds are actually not easy to photograph as they are most of the time hidden, perched high in a tree or frightened by human presence, and they can fly very quickly, whereas audio calls and songs have proved to be easier to collect and very discriminant.…”

Section: Task2: Birdclefmentioning

confidence: 99%

See 1 more Smart Citation

LifeCLEF 2016: Multimedia Life Species Identification Challenges

Joly

Goëau

Glotin

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Using multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcomes.

show abstract

Section: Lifeclef Lab Overviewmentioning

confidence: 99%

Section: Task2: Birdclefmentioning

confidence: 99%

LifeCLEF 2016: Multimedia Life Species Identification Challenges

Joly

Goëau

Glotin

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…For example, the work of Seppo Fagerlund (Fagerlund, 2007) uses support vector machines to classify the different species of birds based on their songs. Similarly, Jim Cai et al (Cai et al, 2007) propose a method recognition based on neural networks to find the membership of a song to a bird class. Our recognition process, inspired by the work of Rabiner (Rabiner & Wilpon, 1979), leverages the same mechanics by means of a clustering algorithm to classify the song.…”

Section: Recognizing the Birdsongmentioning

confidence: 99%

Wildlife Assessment Using Wireless Sensor Networks

Gros-Desormeaux¹,

Hunel²,

Vidot³

2010

Wireless Sensor Networks: Application-Centric Design

View full text Add to dashboard Cite

“…On the other side, the number of experienced botanists is decreasing significantly so that collecting massive sets of plant observations with a valid taxonomic name becomes more and more challenging. In this context, content-based image retrieval and computer vision approaches are considered as one of the most promising solutions to help bridging the gap, as discussed in [1,2,3,4,5]. We therefore see an increasing interest in this transdisciplinary challenge in the multimedia community (e.g.…”

Section: Introductionmentioning

confidence: 99%