An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additivity of Fisher Vectors. The proposed technique is further improved by concatenating these representations with that of deep networks. Based on the results of the simulations on typical data sets, it can be argued that the approach is quite promising for its use of handcrafted features left to dust due to the rise of deep learning.


Real-Time Moving Target Search
Undeger, Cagatay; Polat, Faruk (2007-11-23)
In this paper, we propose a real-time moving target search algorithm for dynamic and partially observable environments, modeled as grid world. The proposed algorithm, Real-time Moving Target Evaluation Search (MTES), is able to detect the closed directions around the agent, and determine the best direction that avoids the nearby obstacles, leading to a moving target which is assumed to be escaping almost optimally. We compared our proposal with Moving Target Search (NITS) and observed a significant improvem...
Face classification with support vector machine
Kepenekci, B; Akar, Gözde (2004-04-30)
A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the ...
A Computationally Efficient Appearance-Based Algorithm for Geospatial Object Detection
Arslan, Duygu; Alatan, Abdullah Aydın (2012-04-27)
A computationally efficient appearance-based algorithm for geospatial object detection is presented and evaluated specifically for aircraft detection from satellite imagery. An aircraft operator exploiting the edge information via gray level differences between the aircraft and its background is constructed with Haar-like polygon regions by using the shape information of the aircraft as an invariant. Fast evaluation of the aircraft operator is achieved by means of integral image. Rotated integral images are...
Automated building detection from satellite images by using shadow information as an object invariant
Yüksel, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2012)
Apart from classical pattern recognition techniques applied for automated building detection in satellite images, a robust building detection methodology is proposed, where self-supervision data can be automatically extracted from the image by using shadow and its direction as an invariant for building object. In this methodology; first the vegetation, water and shadow regions are detected from a given satellite image and local directional fuzzy landscapes representing the existence of building are generate...
An automatic geo-spatial object recognition algorithm for high resolution satellite images
Ergul, Mustafa; Alatan, Abdullah Aydın (2013-09-26)
This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification s...
Citation Formats
I. BUZCU and A. A. Alatan, “FISHER SELECTIVE SEARCH FOR OBJECT DETECTION,” 2016, Accessed: 00, 2020. [Online]. Available: