Lightweight Active Object Retrieval with Weak Classifiers

Abstract

In the last few years, there has been a steadily growing interest in autonomous vehicles and robotic systems. While many of these agents are expected to have limited resources, these systems should be able to dynamically interact with other objects in their environment. We present an approach where lightweight sensory and processing techniques, requiring very limited memory and processing power, can be successfully applied to the task of object retrieval using sensors of different modalities. We use the Hough framework to fuse optical and orientation information of the different views of the objects. In the presented spatio-temporal perception technique, we apply active vision, where, based on the analysis of initial measurements, the direction of the next view is determined to increase the hit-rate of retrieval. The performance of the proposed methods is shown on three datasets loaded with heavy noise.

Active multiview recognition with hidden Markov temporal support

“…While our purpose is similar, in our case the linkage is based on the fusion of visual and orientation information utilizing global descriptors and a modified HMM framework. Naturally, local descriptors are more efficient in generic object classifications, but in case of specific objects we found global descriptors sufficient and also very cost effective considering computational time and memory requirements [7]. Liu et al [13] attack the problem of generating large amount of training data and the common problem of hand-crafted features on various texture-less and surface-smooth objects.…”

“…In [7], we have shown that the area-based CEDD [2] is a robust low-dimensional descriptor for object recognition. CEDD classifies pixels into one of six texture classes (horizontal, vertical, 45 • and 135 • diagonal, non-edge, and non-directional edges) using the MPEG-7 Edge Histogram Descriptor.…”

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

“…Visual sensors are able to acquire a large quantity of visual information from the surroundings around them. Content Based Image Retrieval (CBIR) consists of retrieving images using their content properties from a collection that match a user’s query [ 1 ] based on a similarity measure [ 2 ]. Many research fields, e.g., medical image [ 3 , 4 , 5 ], human retrieval [ 6 ], biological analysis [ 7 , 8 ], agricultural retrieval [ 9 ] and biometric security [ 10 ], achieved interesting results using CBIR techniques.…”