Renato Bueno

Unsupervised scaling of multi-descriptor similarity functions for medical image datasets

Content-based search has proven to be a proper complement to textual queries over medical image databases. In many applications, employing multiple image descriptors and combining the respective distance functions using adequate scale factors improves the retrieval accuracy. However, the existing weighting methods are either exhaustive or supervised. In this paper, we present the Fractal-scaled Product Metric, an unsupervised method to determine a scale factor among features in multi-descriptor image similarity assessment based on the Fractal Theory. The composite distance function obtained is not limited to dimensional image descriptors and enables using scalable indexing structures. Experiments have shown that the proposed method determines near-optimal scale factors for the descriptors involved, and always improves the precision of the results, outperforming the individual descriptors up to 31% on the average precision.

Reducing the complexity of k-nearest diverse neighbor queries in medical image datasets through fractal analysis

Content-Based Image Retrieval (CBIR) Systems allow the search of images by similarity employing a numeric representation automatically or semi-automatically obtained from them to perform the search. Nevertheless, the query result does not always bring what the user expected. In this sense, CBIR systems face the semantic gap problem. One way of overcoming this problem is by the addition of diversity in query execution, so that the user can ask the system to return the most varied images regarding some similarity criteria. However, applying diversity on large datasets has a prohibitive computational cost and, moreover, the result often differs from the expected with a resulting subset that has images with high dissimilarity to the query image. In this paper we propose an approach to reduce the computational cost of Content-Based Image Retrieval systems regarding similarity and diversity criteria. The proposed approach employs dataset fractals analysis to estimate a suitable radius for a database subset to perform a similarity query regarding diversity. It selects closer images to the query center and applies the diversity factor to the subset, providing not only a better comprehension of the impact of the diversity factor to the query result, but also an improvement in execution time.

Query processing over data warehouse using relational databases and NoSQL

Data warehouse (DW) is an important component of Business Intelligence used to support strategic decision making. DW is a subject-oriented, nonvolatile, historical and massive database, which the processing of analytical queries, results in high response times. There known techniques for improving the performance processing of queries on DW. Among them is the use of data fragmentation, materialized views and indices. In addition, the NoSQL is an emerging technology whose main characteristics are improved query processing and data storage, and an alternative to relational databases. In this paper we investigate and compare the implementation of DW using relational databases and NoSQL, considering the Star Schema Benchmark. The results showed that the column-oriented model of the software FastBit showed a better performance, with gains of 25.4% to 99.4% if compared to other models NoSQL and the relational model, in the processing of queries on DW.

Accelerating approximate similarity queries using genetic algorithms

Searching for the exact answer to a similarity query is an expensive process considering computational resources, such as memory and processing time requirements. Moreover, comparison operations over multimedia data is even more expensive than over traditional data such as numbers and small character strings. Therefore, when comparing multimedia data, the comparison computations usually consider some properties extracted from the data elements. In this way, exact queries involving this kind of data return data that is exact regarding the properties compared, but not necessarily exact regarding the multimedia data itself. For example, searching for similar images regarding their colors return images whose color histogram are the most similar, but the retrieved images can be very different regarding, for instance, the shape the objects pictured. Therefore, for applications dealing with complex data types, trading exact answering with query time response can be worthwhile. In this paper we propose to use techniques based on genetic algorithms to allow retrieving data indexed in a metric access methods within a limited, user-defined, amount of time. We show that these techniques lead to much faster execution, without reducing the quality of the answer. We also present experimental evaluation using real datasets, showing that suitable results can be obtained in a fraction of the time required to obtain the exact answer.

Using Visual Analysis to Weight Multiple Signatures to Discriminate Complex Data

Complex data is usually represented through signatures, which are sets of features describing the data content. Several kinds of complex data allow extracting different signatures from an object, representing complementary data characteristics. However, there is no ground truth of how balancing these signatures to reach an ideal similarity distribution. It depends on the analyst intent, that is, according to the job he/she is performing, a few signatures should have more impact in the data distribution than others. This work presents a new technique, called Visual Signature Weighting (ViSW), which allows interactively analyzing the impact of each signature in the similarity of complex data represented through multiple signatures. Our method provides means to explore the tradeoff of prioritizing signatures over the others, by dynamically changing their weight relation. We also present case studies showing that the technique is useful for global dataset analysis as well as for inspecting subspaces of interest.

Improving medical image retrieval through multi-descriptor similarity functions and association rules

Content-based image retrieval (CBIR) systems still face the problem of low precision of system results. To improve the precision of such systems, many image visual extractors have been developed and employed to represent the images. However, the usage of a large number of extractors and consequently, a large number of features, leads to the “dimensionality curse”, where the retrieval performance and the query accuracy diminish. In this paper, we propose a new method, called Statistical Fractal-scaled Product Metric (SFPM), to maximize the accuracy of CBIR systems and speedup similarity queries. The SFPM method combines association rule mining and the Fractal-scaled Product Metric (FPM) [4], to determine a reduced set of features and appropriate scale factors in multi-descriptor image similarity assessment. The FPM is an unsupervised method to determine a scale factor among features in multi-descriptor image similarity assessment based on the Fractal Theory. Experiments have shown that SFPM reduced the feature vector size in up to 65% and improved in up to 27% the query precision when comparing with the use of the FPM technique. The results show that the proposed method SFPM is effective in determining a reduced set of features and a near-optimal set of scale factors for the descriptors involved, and it is well-suited to improve the quality of content-based query in CBIR systems.

Metric Data Analysis Enhanced through Temporal Visualization

The human vision can naturally interpret data in spaces of 2 or 3 dimensions. When data is in higher dimensional spaces, in most cases the visualization is not intuitive. Regarding metric spaces, the interpretation is even harder, since they often do not have a direct spatial representation. However, the need to analyze how metric-represented data evolve over time is pretty common when one needs to understand several phenomena and in decision making processes, as it occurs in medical and agrometeorological applications. This paper presents three interactive techniques to visualize metric data that vary over time. Each one focus on a different way to interpret the temporal information. The first technique shows data evolving in a timeline axis. The second overlaps evolving snapshots of the space showing how the space varies regarding time. The last one does not treat temporal data as a dimension, it is used instead to define the similarity among complex data, employing the new concept of metric-temporal spaces, which seamlessly integrate time and metric data into a single similarity space. Visualization examples with real datasets are presented to show the usefulness of the proposed techniques.

Visual-Interactive k-NDN Method (VIK): A Novel Approach to Visualize and Interact with Content-Based Image Retrieval Systems Regarding Similarity and Diversity

Digital imaging plays an important role in many human activities, such as agriculture and forest management, earth sciences, urban planning, weather forecasting, medical imaging and so on. Processing, exploring and visualizing the inconceivable volumes of such images has turned out to be progressively troublesome. The Content-Based Image Retrieval (CBIR) remains an important issue that finds potential applications, given the place that retrieving digital images similar to a user-defined specification or pattern in huge databases now occupies in the day-to-day. CBIR systems use visual information like color, shape and texture to represent images in feature vectors. In general, there is an inconsistency in the evaluation of similarity between images according to human perception and the results computed by CBIR systems, which is called Semantic Gap. One way to improve CBIR systems is by the addition of techniques to visualize and interact with CBIR regarding similarity and diversity criteria, where the user can participate more actively in the process and steer the results according to its needs. In this paper we present the Visual-Interactive k-NDN Method (ViK): a novel approach to visualize and interact with Content-Based Image Retrieval systems. This paper aims at making use of Visual Data Mining techniques applied to queries in CBIR systems, improving the interpretability of the measure of diversity, applied using fractal analysis, as well as the relevance of results according to the prior knowledge of the user. Therefore, the user takes an active role in the content-based image retrieval, guiding its result and, consequently, reducing the Semantic Gap. Additionally, a better understanding of the diversity and similarity factors involved in the query is supported by visualization and interaction techniques.

An algorithm for effective deletion and a new optimization technique for metric access methods

In our work we developed an algorithm to effectively remove elements from a metric tree, using the MAM Slim-tree as a case study. We also propose a new optimization technique for the structure, based on the deletion algorithm.