Near-Duplicate Image Retrieval Based on Contextual Descriptor

The state of the art of technology for near-duplicate image retrieval is mostly based on the Bag-of-Visual-Words model. However, visual words are easy to result in mismatches because of quantization errors of the local features the words represent. In order to improve the precision of visual words matching, contextual descriptors are designed to strengthen their discriminative power and measure the contextual similarity of visual words. This paper presents a new contextual descriptor that measures the contextual similarity of visual words to immediately discard the mismatches and reduce the count of candidate images. The new contextual descriptor encodes the relationships of dominant orientation and spatial position between the referential visual words and their context. Experimental results on benchmark Copydays dataset demonstrate its efficiency and effectiveness for near-duplicate image retrieval

Influence of Assimilation Effects on Recommender Systems

Recommender systems are a common approach in retail e-commerce to support consumers in finding relevant products. Not surprisingly, user acceptance of personalized product recommendations tends to be higher, leading to higher click rates. Since contextual information also influences user search behavior, we analyze the importance of similarity between recommendations and the underlying context a currently inspected product provides. Using data from a midsize European retail company, we conduct a field experiment and investigate the role of similarities between focal product information and recommendations from a collaborative filtering algorithm. We find that contextual similarity, primarily visual similarity contributes much explanation to consumer click behavior, underlining the importance of contextual and content information in the recommender system\u27s environment

Learning Warps Object Representations in the Ventral Temporal Cortex.

The human ventral temporal cortex (VTC) plays a critical role in object recognition. Although it is well established that visual experience shapes VTC object representations, the impact of semantic and contextual learning is unclear. In this study, we tracked changes in representations of novel visual objects that emerged after learning meaningful information about each object. Over multiple training sessions, participants learned to associate semantic features (e.g., "made of wood," "floats") and spatial contextual associations (e.g., "found in gardens") with novel objects. fMRI was used to examine VTC activity for objects before and after learning. Multivariate pattern similarity analyses revealed that, after learning, VTC activity patterns carried information about the learned contextual associations of the objects, such that objects with contextual associations exhibited higher pattern similarity after learning. Furthermore, these learning-induced increases in pattern information about contextual associations were correlated with reductions in pattern information about the object's visual features. In a second experiment, we validated that these contextual effects translated to real-life objects. Our findings demonstrate that visual object representations in VTC are shaped by the knowledge we have about objects and show that object representations can flexibly adapt as a consequence of learning with the changes related to the specific kind of newly acquired information.This project has received funding to LKT from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 669820), from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007 - 2013)/ERC Grant agreement n° 249640, and a Guggenheim Fellowship to CR.This is the final version of the article. It first appeared from MIT Press via http://dx.doi.org/10.1162/jocn_a_0095

Feature fusion at the local region using localized maximum-margin learning for scene categorization

In the field of visual recognition such as scene categorization, representing an image based on the local feature (e.g., the bag-of-visual-word (BOVW) model and the bag-of-contextual-visual-word (BOCVW) model) has become popular and one of the most successful methods. In this paper, we propose a method that uses localized maximum-margin learning to fuse different types of features during the BOCVW modeling for eventual scene classification. The proposed method fuses multiple features at the stage when the best contextual visual word is selected to represent a local region (hard assignment) or the probabilities of the candidate contextual visual words used to represent the unknown region are estimated (soft assignment). The merits of the proposed method are that (1) errors caused by the ambiguity of single feature when assigning local regions to the contextual visual words can be corrected or the probabilities of the candidate contextual visual words used to represent the region can be estimated more accurately; and that (2) it offers a more flexible way in fusing these features through determining the similarity-metric locally by localized maximum-margin learning. The proposed method has been evaluated experimentally and the results indicate its effectiveness. © 2011 Elsevier Ltd All rights reserved.postprin

Improving content based image retrieval by identifying least and most correlated visual words

In this paper, we propose a model for direct incorporation of im- age content into a (short-term) user profile based on correlations between visual words and adaptation of the similarity measure. The relationships between visual words at different contextual levels are explored. We introduce and compare var- ious notions of correlation, which in general we will refer to as image-level and proximity-based. The information about the most and the least correlated visual words can be exploited in order to adapt the similarity measure. The evaluation, preceding an experiment involving real users (future work), is performed within the Pseudo Relevance Feedback framework. We test our new method on three large data collections, namely MIRFlickr, ImageCLEF, and a collection from British National Geological Survey (BGS). The proposed model is computation- ally cheap and scalable to large image collections

Preserved local but disrupted contextual figure-ground influences in an individual with abnormal function of intermediate visual areas

Visual perception depends not only on local stimulus features but also on their relationship to the surrounding stimulus context, as evident in both local and contextual influences on figure-ground segmentation. Intermediate visual areas may play a role in such contextual influences, as we tested here by examining LG, a rare case of developmental visual agnosia. LG has no evident abnormality of brain structure and functional neuroimaging showed relatively normal V1 function, but his intermediate visual areas (V2/V3) function abnormally. We found that contextual influences on figure-ground organization were selectively disrupted in LG, while local sources of figure-ground influences were preserved. Effects of object knowledge and familiarity on figure-ground organization were also significantly diminished. Our results suggest that the mechanisms mediating contextual and familiarity influences on figure-ground organization are dissociable from those mediating local influences on figure-ground assignment. The disruption of contextual processing in intermediate visual areas may play a role in the substantial object recognition difficulties experienced by LG