Evolving weighting schemes for the Bag of Visual Words

• Published in: Neural computing & applications 2017-05, Vol.28 (5), p.925-939
• Main Authors: Escalante, Hugo Jair, Ponce-López, Víctor, Escalera, Sergio, Baró, Xavier, Morales-Reyes, Alicia, Martínez-Carranza, José
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Intelligence for Vision and Robotics; Computational Science and Engineering; Computer Science; Computer vision; Data mining; Data Mining and Knowledge Discovery; Evolutionary algorithms; Image Processing and Computer Vision; Machine learning; Probability and Statistics in Computer Science; Representations; Weighting
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2223-x

The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g., term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.