ADAM - A Database and Information Retrieval System for Big Multimedia Collections

Giangreco, Ivan; Kabary, Ihab Al; Schuldt, Heiko

doi:10.1109/bigdata.congress.2014.66

Cited by 18 publications

(9 citation statements)

References 11 publications

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“…ADAM uses PostgreSQL and comes with the Vector Approximation-File [14] indexing and k nearest neighbor search. The evaluation of ADAM has shown that it is able to handle big multimedia collections of multiple million objects and keep the retrieval time well below a few seconds (e.g., for 14 million elements each storing 144 dimensions, ADAM returns results on average in 0.55 seconds for the 100 most similar objects [5]). …”

Section: Databasementioning

confidence: 99%

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Semantic Sketch-Based Video Retrieval with Autocompletion

Tanase

Giangreco

Rossetto

et al. 2016

Companion Publication of the 21st International Conference on Intelligent User Interfaces

Self Cite

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The IMOTION system is a content-based video search engine that provides fast and intuitive known item search in large video collections. User interaction consists mainly of sketching, which the system recognizes in real-time and makes suggestions based on both visual appearance of the sketch (what does the sketch look like in terms of colors, edge distribution, etc.) and semantic content (what object is the user sketching). The latter is enabled by a predictive sketch-based UI that identifies likely candidates for the sketched object via state-of-the-art sketch recognition techniques and offers on-screen completion suggestions. In this demo, we show how the sketch-based video retrieval of the IMOTION system is used in a collection of roughly 30,000 video shots. The system indexes collection data with over 30 visual features describing color, edge, motion, and semantic information. Resulting feature data is stored in ADAM, an efficient database system optimized for fast retrieval.

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Section: Databasementioning

confidence: 99%

“…IMOTION is based on the distributed storage system ADAM [5] that allows to easily manage, organise, and query multimedia objects, i.e., the corresponding structured metadata, as well as the extracted high-dimensional feature vectors. ADAM uses PostgreSQL and comes with the Vector Approximation-File [14] indexing and k nearest neighbor search.…”

Section: Databasementioning

confidence: 99%

Semantic Sketch-Based Video Retrieval with Autocompletion

Tanase

Giangreco

Rossetto

et al. 2016

Companion Publication of the 21st International Conference on Intelligent User Interfaces

Self Cite

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show abstract

“…In [22], author describe that the data is not big in terms of their volume but also include the queries that are performed to access that data. There are many strategies through which we can search data using different keywords.…”

Section: A Research Paper Summariesmentioning

confidence: 99%

Fast and Efficient In-Memory Big Data Processing

Malik¹,

Maryam²,

Khalid³

et al. 2019

IJACSA

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With the passage of time, the data is growing exponentially and the mostly endured areas are social media networks, media hosting applications, and servers. They have thousands of Tera-bytes of data and the efficient systems, however, they are as yet confronting issue to oversee such volume of information and its size is growing each day. Data systems retrieve information with less time of In-memory. Instead of each factor data systems are required to define good usage of cache and fast memory access with help of optimization. The proposed technique to solve this problem can be the optimal indexing technique with better and efficient utilization of Cache and having less overhead of DRAM with the goal that energy can also be saved for the high-end servers.

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“…All feature vectors along with meta-data are stored in the database and information retrieval system ADAM [16] which is built upon PostgreSQL and is capable of performing efficient vector space retrieval together with Boolean retrieval.…”

Section: Scoring Video Search Tools In Vbs2015 41 Imotionmentioning

confidence: 99%

Interactive video search tools: a detailed analysis of the video browser showdown 2015

Cobârzan

Schoeffmann

Bailer

et al. 2016

Multimed Tools Appl

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Interactive video retrieval tools developed over the past few years are emerging as powerful alternatives to automatic retrieval approaches by giving the user more control as well as more responsibilities. Current research tries to identify the best combinations of image, audio and text features that combined with innovative UI design maximize the tools (2017) 76:5539-5571 performance. We present the last installment of the Video Browser Showdown 2015 which was held in conjunction with the International Conference on MultiMedia Modeling 2015 (MMM 2015) and has the stated aim of pushing for a better integration of the user into the search process. The setup of the competition including the used dataset and the presented tasks as well as the participating tools will be introduced . The performance of those tools will be thoroughly presented and analyzed. Interesting highlights will be marked and some predictions regarding the research focus within the field for the near future will be made.

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ADAM - A Database and Information Retrieval System for Big Multimedia Collections

Cited by 18 publications

References 11 publications

Semantic Sketch-Based Video Retrieval with Autocompletion

Semantic Sketch-Based Video Retrieval with Autocompletion

Fast and Efficient In-Memory Big Data Processing

Interactive video search tools: a detailed analysis of the video browser showdown 2015

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