This paper proposes two novel image similarity measures for fast indexing via locality sensitive hashing. The similarity measures are applied and evaluated in the context of near duplicate image detection. The proposed method uses a visual vocabulary of vector quantized local feature descriptors (SIFT) and for retrieval exploits enhanced min-Hash techniques. Standard min-Hash uses an approximate set intersection between document descriptors was used as a similarity measure. We propose an efficient way of exploiting more sophisticated similarity measures that have proven to be essential in image / particular object retrieval. The proposed similarity measures do not require extra computational effort compared to the original measure. We focus primarily on scalability to very large image and video databases, where fast query processing is necessary. The method requires only a small amount of data need be stored for each image. We demonstrate our method on the TrecVid 2006 data set which contains approximately 146K key frames, and also on challenging the University of Kentucky image retrieval database.
In this paper, we investigate how to establish the relationship between semantic concepts based on the large-scale realworld click data from image commercial engine, which is a ...
Abstract The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints is studied. A new set of ...
The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasin...
In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector. Many different descriptor...
We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a s...
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