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Multi-feature search is an effective approach to similarity search. Unfortunately, the search efficiency decreases with the number of features. Several indexing approaches aim to achieve efficiency by incrementally reducing the approximation error of aggregated distance bounds. They apply heuristics to determine the distance computations order and update the object's aggregated bounds after each computation. However, the existing indexing approaches suffer from several drawbacks. They use the same computation order for all objects, do not support important types of aggregation functions and do not take the varying CPU and I/O costs of different distance computations into account. To resolve these problems, we introduce a new heuristic to determine an efficient distance computation order for each individual object. Our heuristic supports various important aggregation functions and calculates cost-benefit-ratios to incorporate the varying computation costs of different distance functions. The experimental evaluation reveals that our heuristic outperforms state-of-the-art approaches in terms of the number of distance computations as well as search time.
The flexibility of an indexing approach plays an important role for its applicability, especially for logic-based similarity search. A flexible approach allows the use of the same precomputed index structure even if query elements like weights, operators, monotonicity or used features of the aggregation function change in the search process (e.g., when using relevance feedback). While state- of-the-art approaches typically fulfill some of the needed flexibility requirements, none provides all of them. Consequently, this paper present FlexiDex, an efficient indexing approach for logic-based similarity search that is more flexible and also more efficient than known techniques.
Filter refinement is an efficient and flexible indexing approach to similarity search with multiple features. However, the conventional refinement phase has one major drawback: when an object is refined, the partial distances to the query object are computed for all features. This frequently leads to more distance computations being executed than necessary to exclude an object. To address this problem, we introduce partial refinement, a simple, yet efficient improvement of the filter refinement approach. It incrementally replaces partial distance bounds with exact partial distances and updates the aggregated bounds accordingly each time. This enables us to exclude many objects before all of their partial distances have been computed exactly. Our experimental evaluation illustrates that partial refinement significantly reduces the number of required distance computations and the overall search time in comparison to conventional refinement and other state-of-the-art techniques.
PythiaSearch ist ein interaktives Multimedia-Retrieval-System. Es vereint verschiedene Suchstrategien, diverse Visualisierungen und erlaubt eine Personalisierung der Retrieval-Ergebnisse mittels eines Präferenz-basierten Relevance Feedbacks. Das System nutzt die probabilistische Anfragesprache CQQL und erlaubt eine multi-modale Anfragedefinition basierend auf Bildern, Texten oder Metadaten.
An Evaluation of the Principle of Polyrepresentation in Multimodal and Content-based Image Retrieval
(2012)
An Extensible Personal Photograph Collection for Graded Relevance Assessments and USER Simulation
(2011)
We present a Matlab toolbox, called “FockBox”, handling
Fock spaces and objects associated with Fock spaces: scalars, ket and
bra vectors, and linear operators. We give brief application examples
from computational linguistics, semantics processing, and quantum logic,demonstrating the use of the toolbox.