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Many image retrieval systems adopt the bag-of-words model and rely on matching of local descriptors. However, these descriptors of keypoints, such as SIFT, may lead to false matches, since they do not consider the contextual information of the keypoints. In this paper, we incorporate the cues of meaningful regions where local descriptors are extracted. We describe a matching region estimation (MRE) method to find appropriate matching regions for local descriptor matching pairs.

Person reidentification refers to the task of recognizing the same person
under different non-overlapping camera views. Presently, person
reidentification based on metric learning is proved to be effective among
various techniques, which exploits the labeled data to learn
a subspace that maximizes the inter-person divergence while minimizes
the intra-person divergence. However, these methods fail to
take the different impacts of various instances and local features into
account. To address this issue, we propose to learn a projection matrix

As Internet users increasingly post images to express their daily sentiment and emotions, the analysis of sentiments in user-generated images is of increasing importance for developing several applications. Most conventional methods of image sentiment analysis focus on the design of visual features, and the use of text associated to the images has not been sufficiently investigated. This paper proposes a novel approach that exploits latent correlations among multiple views: visual and textual views, and a sentiment view constructed using SentiWordNet.