Download PDF by Bogdan Ionescu, Jenny Benois-Pineau, Tomas Piatrik, Georges: Fusion in Computer Vision: Understanding Complex Visual

Download PDF by Bogdan Ionescu, Jenny Benois-Pineau, Tomas Piatrik, Georges: Fusion in Computer Vision: Understanding Complex Visual

By Bogdan Ionescu, Jenny Benois-Pineau, Tomas Piatrik, Georges Quénot

This booklet provides an intensive evaluation of fusion in laptop imaginative and prescient, from an interdisciplinary and multi-application point of view, describing profitable techniques, evaluated within the context of foreign benchmarks that version real looking use situations. positive aspects: examines overdue fusion methods for inspiration acceptance in photographs and video clips describes the translation of visible content material by means of incorporating versions of the human visible approach with content material figuring out equipment investigates the fusion of multi-modal positive aspects of alternative semantic degrees, in addition to result of semantic notion detections, for example-based occasion acceptance in video proposes rotation-based ensemble classifiers for high-dimensional facts, which inspire either person accuracy and variety in the ensemble stories application-focused concepts of fusion in video surveillance, biomedical details retrieval, and content material detection in video clips discusses the modeling of mechanisms of human interpretation of complicated visible content material.

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Liu N, Dellandréa E, Tellez B, Chen L (2011) Evaluation of features and combination approaches for the classification of emotional semantics in images. In: International conference on computer vision, theory and applications (VISAPP) 44. Liu N, Dellandréa E, Tellez B, Chen L, Chen L (2011) Associating textual features with visual ones to improve affective image classification. In: Proceedings of ACII, vol 1, pp 195–204 45. Valdez P, Mehrabian A (1994) Effects of color on emotions. J Exp Psychol Gen 123:394–409 46.

Textual_model_1: the combination of the top 4 features among the 10 textual features for each concept based on the weighted score SWFL scheme. 2. multimodal_model_2: the combination of the top 21 features among 34 visual and textual features for each concept based on the weighted score SWFL scheme. 3. textual_model_3: the combination of the top 5 features among the 10 textual features for each concept based on the weighted score SWFL scheme. 20 N. Liu et al. 86 4. visual_model_4: the combination of the top 5 features among the 24 visual features for each concept based on the weighted score SWFL scheme.

It ranked closely to the first place obtained by TUBFI (TU Berlin and Fraunhofer FIRST) [18], who employed a non-sparse multiple kernel learning to combine kernels produced from various BoW features including a soft mapping of textual Bag-of-Words (BoW) and Markov random walks based on frequent Flickr user tags. 6 Results at ImageCLEF 2012 Photo Annotation Challenge The ImageCLEF 2012 photo annotation task continues along the same lines as previous years. It provides 15,000 images for training and 10,000 images for testing, and has 94 concepts to be detected, where a few old concepts have been removed and a few new ones have been added.

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