New strategy for Turbo Similarity Searching: Implementation and testing

Malim, Nurul Hashimah Ahamed Hassain; Pei-Chia, Yong; Arif, Shereena M.

doi:10.1109/icacsis.2013.6761572

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…NN is a proximity search for finding closest or similar point in which the points are separated into several separate classes. Despite its simplicity, NN has been successful in a large number of classification problems such as in [23]- [26]. During a query, these metric values will be compared so the process of retrieving or browsing of related images could be performed.…”

Section: Landscape Painting's Scene Categorizationmentioning

confidence: 99%

Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

Othman

Wook

Arif³

2016

International Journal on Advanced Science, Engineering and Information Technology

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In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on the Harmonic Mean of the precision and recall, also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds. The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.

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Section: Landscape Painting's Scene Categorizationmentioning

confidence: 99%

Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

Othman

Wook

Arif³

2016

International Journal on Advanced Science, Engineering and Information Technology

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“…(2), the new strategy as described earlier in [16] has added in another two layers making it five layers as shown in Fig. (3).…”

Section: Methodsmentioning

confidence: 99%

The Effect of Adding Indirect Relationship to Turbo Similarity Searching

Malim¹,

Pei-Chia²,

Al-Laila³

et al. 2016

PHARMSCI

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Background:Turbo Similarity Searching (TSS) has been proved as one of the effective and simple searching method in Cheminformatics. Emerging from the conventional similarity searching, TSS depended on the concept of fusion where relationship between the target being sought and the compound in the database are indirect. Previous works has looked at only one level of indirect relationship and indicates that there are further potential that more levels of such relationship be added to TSS to increase its ability to recover more actives. Hence, in this work, we aimed to investigate the impact of the indirect relationship on TSS. Method:This study has further investigated the enhancement of TSS using additional layers of indirect relationship and fusion process. We implemented TSS by adding another layer of fusion between the target and database compound. Results:The experiments with MDDR database showed that the proposed new strategy described in this paper provide a way of enhancing the effectiveness of the TSS process in chemical databases. The experiments also showed that the increases in performance are particularly better when the sought actives are structurally diverse. Conclusion:We may conclude that the additional layers do increase the recall of TSS. Hence, the new TSS strategy could be used as an alternative to the old TSS.

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“…Similarity evaluation is performed by taking the top 1% of the ranked list and matching it against the known actives where a recall value is returned. Further details on recall can be found in [23]. We will not dwell much on the recall values because we are more interested in improving the timing of the searching process.…”

Section: Similarity Searchingmentioning

confidence: 99%

Accelerating Group Fusion for Ligand-Based Virtual Screening on Multi-core and Many-core Platforms

2016

J Inf Process Syst

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The performance issues of screening large database compounds and multiple query compounds in virtual screening highlight a common concern in Chemoinformatics applications. This study investigates these problems by choosing group fusion as a pilot model and presents efficient parallel solutions in parallel platforms, specifically, the multi-core architecture of CPU and many-core architecture of graphical processing unit (GPU). A study of sequential group fusion and a proposed design of parallel CUDA group fusion are presented in this paper. The design involves solving two important stages of group fusion, namely, similarity search and fusion (MAX rule), while addressing embarrassingly parallel and parallel reduction models. The sequential, optimized sequential and parallel OpenMP of group fusion were implemented and evaluated. The outcome of the analysis from these three different design approaches influenced the design of parallel CUDA version in order to optimize and achieve high computation intensity. The proposed parallel CUDA performed better than sequential and parallel OpenMP in terms of both execution time and speedup. The parallel CUDA was 5-10x faster than sequential and parallel OpenMP as both similarity search and fusion MAX stages had been CUDA-optimized.

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New strategy for Turbo Similarity Searching: Implementation and testing

Cited by 4 publications

References 19 publications

Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

The Effect of Adding Indirect Relationship to Turbo Similarity Searching

Accelerating Group Fusion for Ligand-Based Virtual Screening on Multi-core and Many-core Platforms

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