Bag of Attributes for Video Event Retrieval
Leonardo A. Duarte, Ot\'avio A. B. Penatti, and Jurandy Almeida

TL;DR
This paper introduces the Bag-of-Attributes (BoA) model for video representation, leveraging semantic features from deep neural networks to improve video event retrieval with compact, high-level feature vectors.
Contribution
The paper proposes a novel BoA model that encodes videos into semantic attribute vectors using pre-trained CNNs, enhancing retrieval performance and compactness.
Findings
BoA achieves comparable or better retrieval results than baselines.
BoA provides a more compact video representation.
Semantic features from CNNs improve video retrieval accuracy.
Abstract
In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can be understood as a high-level codebook. This model is used to map low-level frame vectors into high-level vectors (e.g., classifier probability scores). Then, we apply pooling operations to the frame vectors to create the final bag of attributes for the video. In the BoA representation, each dimension corresponds to one category (or attribute) of the semantic space. Other interesting properties are: compactness, flexibility regarding the classifier, and ability to encode multiple semantic…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsConvolution · *Communicated@Fast*How Do I Communicate to Expedia? · Dropout · Dense Connections · Softmax · OverFeat · Max Pooling
