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Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition and classification applications. Principal Component Analysis (PCA) is one of the popular methods used, and can be shown to be optimal using different optimality criteria. However, it has the disadvantage that measurements from all the original features are(More)
The state-of-the-art image retrieval approaches represent images with a high dimensional vector of visual words by quantizing local features, such as SIFT, in the descriptor space. The geometric clues among visual words in an image is usually ignored or exploited for full geometric verification, which is computationally expensive. In this paper, we focus on(More)
Concept-based multimedia search has become more and more popular in multimedia information retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. , there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we(More)
Bag-of-Visual-Words model is popular in large-scale image search. However, traditional Bag-of-Visual-Words model does not capture the geometric context among local features in images. To fully explore geometric context of all visual words in images, efficient global geometric verification methods are demanded. In this paper, we propose a novel geometric(More)
License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes,(More)
Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visual-words model does not capture the geometric context among local features in images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global(More)
Searching for relevant 3D models based on hand-drawn sketches is both intuitive and important for many applications, such as sketch-based 3D modeling and recognition. We propose a sketch-based 3D model retrieval algorithm by utilizing viewpoint entropy-based adaptive view clustering and shape context matching. Different models have different visual(More)
Bag-of-Words (BoW) model based on SIFT has been widely used in large scale image retrieval applications. Feature quantization plays a crucial role in BoW model, which generates visual words from the high dimensional SIFT features, so as to adapt to the inverted file structure for indexing. Traditional feature quantization approaches suffer several problems:(More)
Sketch based 3D shape retrieval has become an important research topic in content based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small scale and large scale benchmarks, respectively. Six and five (nine in total) distinct sketch based 3D(More)
In recent years, constructing mathematical models for visual concepts by using content features, i.e., color, texture, shape, or local features, has led to the fast development of concept-based multimedia retrieval. In concept-based multimedia retrieval, defining a good lexicon of high-level concepts is the first and important step. However, which concepts(More)