We thank the reviewers for detailed and helpful reviews. This is incremental overall ... low technical novelty..." We indeed adopt a common CNN architecture (specifically VGG) as is now typical in vision tasks. However, we adopt the network to a new problem domain: generating segmentation object proposals. For this problem domain, all previous approaches operate by merging or grouping superpixels or edges (with no or only minor data-driven components). Instead, we formulate the problem directly as a learning problem.

In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a Mutual Information CO-training framework for selective search with minimal supervision using the search logs. After training, MICO does not only cluster the documents, but also routes unseen queries to the relevant clusters for efficient retrieval. In our empirical experiments, MICO significantly improves the performance on multiple metrics of selective search and outperforms a number of existing competitive baselines.

This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects object candidates in the document page images. Next, deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binary code representations. Finally, candidate images are ranked by computing the feature similarity with a given input query. A robust experimental protocol evaluates the proposed approach considering each representation scheme (real-valued and binary code) on the DocExplore image database. The experimental results show that the proposed deep models compare favorably to the state-of-the-art image retrieval approaches for images of historical documents, outperforming other deep models by 2.56 percentage points using the same techniques for pattern spotting. Besides, the proposed approach also reduces the search time by up to 200x and the storage cost up to 6,000x when compared to related works based on real-valued representations.

Imagine Google Photos: for all of the pictures you have, how do you label those by objects. Do you want to tag them one by one? How about automated cars driving? How do they detect pedestrians, cars, traffic lights, and impending obstacles? In recent years, image classification has gained huge traction especially with CNN and disruptive applications (e.g: self driving cars).

In this tutorial, you will learn how to build an R-CNN object detector using Keras, TensorFlow, and Deep Learning. Today's tutorial is the final part in our 4-part series on deep learning and object detection: What if we wanted to train an object detection network on our own custom datasets? How can we train that network using Selective Search search? And how will using Selective Search change our object detection inference script? In fact, these are the same questions that Girshick et al. had to consider in their seminal deep learning object detection paper Rich feature hierarchies for accurate object detection and semantic segmentation. Each of these questions will be answered in today's tutorial -- and by the time you're done reading it, you'll have a fully functioning R-CNN, similar (yet simplified) to the one Girshick et al. implemented! To learn how to build an R-CNN object detector using Keras and TensorFlow, just keep reading.

Region with Convolutional Neural Network (R-CNN) is proposed by Girshick et al. in 2013. It changed the object detection field fundamentally. By leveraging selective search, CNN and SVM, Girshick et al. achieved a very good result in VOC 2012. This story will introduce R-CNN while later series will cover Fast R-CNN, Faster R-CNN and Mask R-CNN that is introduced by Girshick and other team members as well. Besides, there are other objection detection approaches such as Single-Shot Object Detector (SSD) and You Only Look Once (YOLO). The objective of image classification is classifying the category of the whole image.

Object detection problems pose several unique obstacles beyond what is required for image classification. Five such challenges are reviewed in this post along with researchers' efforts to overcome these complications. The field of computer vision has experienced substantial progress recently owing largely to advances in deep learning, specifically convolutional neural nets (CNNs). Image classification, where a computer classifies or assigns labels to an image based on its content, can often see great results simply by leveraging pre-trained neural nets and fine-tuning the last few throughput layers. Classifying and finding an unknown number of individual objects within an image, however, was considered an extremely difficult problem only a few years ago.

Objects of interest occupying minority classes, therefore, receive more significance and see improved accuracy. Object detection is customarily considered to be much harder than image classification, particularly because of these five challenges: dual priorities, speed, multiple scales, limited data, and class imbalance. Researchers have dedicated much effort to overcome these difficulties, yielding oftentimes amazing results; however, significant challenges still persist. Basically all object detection frameworks continue to struggle with small objects, especially those bunched together with partial occlusions. Real-time detection with top-level classification and localization accuracy remains challenging, and practitioners must often prioritize one or the other when making design decisions. Video tracking may see improvements in the future if some continuity between frames is assumed rather than processing each frame individually. Furthermore, an interesting enhancement that may see more exploration would extend the current two-dimensional bounding boxes into three-dimensional bounding cubes. Even though many object detection obstacles have seen creative solutions, these additional considerations–and plenty more–signal that object detection research is certainly not done!

In this post, I shall explain object detection and various algorithms like Faster R-CNN, YOLO, SSD. We shall start from beginners' level and go till the state-of-the-art in object detection, understanding the intuition, approach and salient features of each method. Image classification takes an image and predicts the object in an image. What would our model predict? To solve this problem we can train a multi-label classifier which will predict both the classes(dog as well as cat).