AH+-Tree: An Efficient Multimedia Indexing Structure for Similarity Queries

Fleites, Fausto C.; Chen, Shu‐Ching; Chatterjee, Kasturi

doi:10.1109/ism.2011.20

Cited by 3 publications

(8 citation statements)

References 7 publications

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“…As the backbone of product retrieval, this component presents the AH + -tree [39,40], an efficient, height-balanced, multi-dimensional, tree-based index structure for multimedia data. The AH + -tree utilizes high-level affinity information from the Markov Model Mediator (MMM) [41] during the retrieval process to address the semantic gap and the subjectivity of the users' perception.…”

Section: Product Retrieval Via Semantic Index Methodsmentioning

confidence: 99%

“…To efficiently address these challenges in the retrieval task, the proposed framework presents the AH + -tree [40][39], a height-balanced, multi-dimensional tree structure for multimedia data that utilizes semantic information to address the challenges inherent to multimedia retrieval. The semantic information consists of high-level affinity relationships that numerically represent how semantically related multimedia objects are from the consumers perspective, based on the idea that the more frequent two images are accessed together, the more related they are.…”

Section: Product Retrievalmentioning

confidence: 99%

“…This chapter presents a novel index structure called the AH + -tree for image data that addresses such problems during the retrieval process by efficiently incorporating semantic information [40][39]. By indexing a product database, the AH + -tree provides product retrieval via similarity queries, e.g., nearest neighbor search.…”

Section: Product Retrievalmentioning

confidence: 99%

“…To address the I/O overhead problems of the AH-Tree, this chapter presents the AH + -tree [40][39], which utilizes the affinity values in a novel way to avoid having to perform the process of affinity promotion while still providing the same accuracy of the AH-Tree. Instead of traversing the whole tree to promote affinity values, the AH + -tree efficiently stores and makes use of the affinity information along with object-to-node and node-to-node relationships to identify the sub-trees that need to be trimmed out during the retrieval process.…”

Section: Tree-based Indexingmentioning

confidence: 99%

“…The following two paragraphs describe the algorithms for range and kNN queries. Please refer to Fleites et al [40] for an in-depth description of these algorithms.…”

Section: Similarity Queriesmentioning

confidence: 99%

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A Scalable Multimedia Content Processing Framework with Application to TV Shopping

Fleites¹

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The advent of smart TVs has reshaped the TV-consumer interaction by combining TVs with mobile-like applications and access to the Internet. However, consumers are still unable to seamlessly interact with the contents being streamed. An example of such limitation is TV shopping, in which a consumer makes a purchase of a product or item displayed in the current TV show. Currently, consumers can only stop or pause the current show and attempt to find a similar item in the Web or an actual store by providing a description of the product. It would be more convenient if the consumer could interact with the TV to purchase interesting items.Towards the realization of TV shopping, this dissertation proposes a scalable multimedia content processing framework. Two main challenges in TV shopping are addressed: the efficient detection of interesting products in the content stream, and the retrieval of similar products given a consumer-selected product. The proposed framework consists of three components. The first component performs computational and temporal aware multimedia abstraction. The goal of this component is to select a reduced number of frames that summarize the important information in the video stream. By both reducing the number of frames and taking into account the computational cost of the subsequent detection phase, the abstraction component allows the efficient detection of products in the stream. The second component realizes vi the detection phase. It executes scalable product detection using multi-cue optimization. Additional information cues are formulated into an optimization problem that allows the detection of complex products, i.e., those that do not have a rigid form and can appear in various poses. After the second component identifies products in the video stream, the consumer can select an interesting one for which similar ones must be located in a product database. To this end, the third component of the framework consists of an efficient, multi-dimensional, tree-based indexing method for multimedia databases. The proposed index mechanism serves as the backbone of the search.Moreover, it is able to efficiently bridge the semantic gap and perception subjectivity issues during the retrieval process to provide more relevant results.

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Section: Product Retrieval Via Semantic Index Methodsmentioning

confidence: 99%

Section: Product Retrievalmentioning

confidence: 99%

Section: Product Retrievalmentioning

confidence: 99%

Section: Tree-based Indexingmentioning

confidence: 99%

“…The following two paragraphs describe the algorithms for range and kNN queries. Please refer to Fleites et al [40] for an in-depth description of these algorithms.…”

Section: Similarity Queriesmentioning

confidence: 99%

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A Scalable Multimedia Content Processing Framework with Application to TV Shopping

Fleites¹

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In-Memory Distributed Indexing for Large-Scale Media Data Retrieval

Liu

Scott

et al. 2017

2017 IEEE International Symposium on Multimedia (ISM)

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Multimedia Big Data Analytics

et al. 2018

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With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

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AH+-Tree: An Efficient Multimedia Indexing Structure for Similarity Queries

Cited by 3 publications

References 7 publications

A Scalable Multimedia Content Processing Framework with Application to TV Shopping

A Scalable Multimedia Content Processing Framework with Application to TV Shopping

In-Memory Distributed Indexing for Large-Scale Media Data Retrieval

Multimedia Big Data Analytics

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