Distance approximation techniques to reduce the dimensionality for multimedia databases

Recently, databases have been used to store multimedia data such as images, maps, video clips, and music clips. In order to search them, they should be represented by various features, which are composed of high-dimensional vectors. As a result, the dimensionality of data is increased considerably, which causes ‘the curse of dimensionality’. The increase of data dimensionality causes poor performance of index structures. To overcome the problem, the research on the dimensionality reduction has been conducted. However, some reduction methods do not guarantee no false dismissal, while others incur high computational cost. This paper proposes dimensionality reduction techniques that guarantee no false dismissal while providing efficiency considerable by approximating distances with a few values. To provide the no false dismissal property, approximated distances should always be smaller than original distances. The Cauchy–Schwarz inequality and two trigonometrical equations are used as well as the dimension partitioning technique is applied to approximate distances in such a way to reduce the difference between the approximated distance and the original distance. As a result, the proposed techniques reduce the candidate set of a query result for efficient query processing.     

Hybrid domain adaptation for sensor-based human activity recognition in a heterogeneous setup with feature commonalities

Pattern Analysis and Applications - Common approaches in the cross-domain sensor-based human activity recognition are based on the homogeneous domain adaptation which relies on the assumption that...     

Confidence interval approach to feature re-weighting

Relevance feedback is commonly incorporated into content-based image retrieval systems with the objective of improving retrieval accuracy via user feedback. One effective method for improving retrieval performance is to perform feature re-weighting based on the obtained feedback. Previous approaches to feature re-weighting via relevance feedback assume the feature data for images can be represented in fixed-length vectors. However, many approaches are invalidated with the recent development of features that cannot be represented in fixed-length vectors. In addition, previous approaches use only the information from the set of images returned in the latest query result for feature re-weighting. In this paper, we propose a feature re-weighting approach that places no restriction on the representation of feature data and utilizes the aggregate set of images returned over the iterations of retrieval to obtain feature re-weighting information. The approach analyzes the feature distances calculated between the query image and the resulting set of images to approximate the feature distances for the entire set of images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how the feature representations are structured. The experimental results show the effectiveness of the proposed approach and in comparisons with other work, it is shown that our approach outperforms previous work.

Influence of Ga addition on the methanol synthesis activity of Cu/ZnO catalyst in the presence and absence of alumina

The influence of Ga addition to Cu/ZnO (CZ) methanol synthesis catalyst was investigated in the presence (CZA-Ga) and absence (CZ-Ga) of alumina. The reducibility of CZA-Ga was found to be more facile than CZ-Ga. The acidic strength of CZA-Ga was considerably decreased compared to CZA and CZ-Ga. In the presence of alumina, the addition of Ga to CZ improved the textural properties, resisted agglomeration of copper and showed higher activity and stability in methanol synthesis reaction from syngas. The easy reducibility and the appropriate acidity can be ascribed to the higher activity with high yield to methanol observed on CZA-Ga.     

Automatic evaluation of cardiac hypertrophy using cardiothoracic area ratio in chest radiograph images

To evaluate the cardiac hypertrophy from chest radiograph images, radiologists usually examine the cardiothoracic ratio (frequently called CTR) which is a standard diagnostic index. The CTR is computed by the maximum transverse diameter of the heart shadow divided by the maximum transverse diameter of right and left lung boundaries. In this paper, we present a method to evaluate the cardiac hypertrophy by comparing the area of heart with that of lung, instead of the cardiothoracic ratio to get more desirable diagnostic results. We introduce a new index, a cardiothoracic area ratio (CTAR), which is computed by dividing the area of heart region by the area of lung region of specific interest. We first segment a chest region of interest in a radiograph image and then automatically compute the traditional CTR and the CTAR to evaluate the cardiac hypertrophy. And finally, we provide the visual presentation of those ratios on the chest radiograph image. The experimental results using a set of radiograph images show that the proposed method can be used effectively for determining the cardiac hypertrophy in a real-time diagnostic environment. It provides the higher discrimination power than the CTR to identify hypertrophied hearts by recognizing the heart enlargement. It also can be used together with the traditional CTR as a complementary measure when it is difficult to determine abnormalities by the CTR, reducing the rate of wrong diagnosis.