Fine-grained object recognition and zero-shot learning in multispectral imagery

Sumbul, Gencer
Cinbiş, Ramazan Gökberk
We present a method for fine-grained object recognition problem, that aims to recognize the type of an object among a large number of sub-categories, and zero-shot learning scenario on multispectral images. In order to establish a relation between seen classes and new unseen classes, a compatibility function between image features extracted from a convolutional neural network and auxiliary information of classes is learnt. Knowledge transfer for unseen classes is carried out by maximizing this function. Performance of the model (15.2%) evaluated with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information is promisingly better than the other methods for 16 test classes.


Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Sumbul, Gencer; Cinbiş, Ramazan Gökberk; Aksoy, Selim (2018-02-01)
Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low betweenclass variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists f...
Multisource region attention network for fine-grained object recognition in remote sensing imagery
Sümbül, Gencer; Cinbiş, Ramazan Gökberk; Aksoy, Selim (Institute of Electrical and Electronics Engineers (IEEE), 2019-07)
Fine-grained object recognition concerns the identification of the type of an object among a large number of closely related subcategories. Multisource data analysis that aims to leverage the complementary spectral, spatial, and structural information embedded in different sources is a promising direction toward solving the fine-grained recognition problem that involves low between-class variance, small training set sizes for rare classes, and class imbalance. However, the common assumption of coregistered ...
A method for quadruplet sample selection in deep feature learning Derin Öznitelik Öǧrenme için Dördüz Örnek Seçme Yöntemi
Karaman, Kaan; Gundogdu, Erhan; Koc, Aykut; Alatan, Abdullah Aydın (2018-07-05)
Recently, the deep learning based feature learning methodologies have been developed to recognize the objects in fine-grained detail. In order to increase the discriminativeness and robustness of the utilized features, this paper proposes a sample selection methodology for the quadruplet based feature learning. The feature space is manipulated by using the hierarchical structure of the training set. In the training process, the quadruplets are selected by considering the distances between the samples in the...
A rule-based method for object segmentation in video sequences
Alatan, Abdullah Aydın; Onural, L (1997-01-01)
Object segmentation and tracking are problems within the scope of MPEG-4 and MPEG-7 standardization activities. A novel algorithm for both object segmentation and tracking is presented. The algorithm fuses motion, color, and accumulated previous segmentation data at 'region level', in contrast to conventional 'pixel level' approaches. The information fusion is achieved by a rule-based region processing unit which intelligently utilizes the motion information to locate the objects in the scene, the color inf...
Rescoring detections based on contextual scores in object detection
Zorlu, Ersan Vural; Akbaş, Emre; Department of Computer Engineering (2019)
To detect objects in an image, current state-of-the-art object detectors firstly definecandidate object locations, and then classify each of them into one of the predefinedcategories or as background. They do so by using the visual features extracted locallyfrom the candidate locations; omitting the rich contextual information embedded inthe whole image. Contextual information can be utilized to complement the informa-tion extracted locally and thereby to improve object detection accuracy. Researchershave p...
Citation Formats
G. Sumbul, R. G. Cinbiş, and S. AKSOY, “Fine-grained object recognition and zero-shot learning in multispectral imagery,” 2018, Accessed: 00, 2020. [Online]. Available: